2.4 Main Tasks
NER
Classification of events
Masking and coding tasks
Whether you are a seasoned data scientist or new to machine learning, this guide is structured to help you navigate through the varied functionalities of ConfliBERT with ease. For a hands-on introduction and demonstration, jump straight to our Demo usage. If you’re interested in applying ConfliBERT for Named Entity Recognition, see the section on NER (Named Entity Recognition) with ConfliBERT. To dive into text classification, visit the section on Classification with ConfliBERT. For masking and coding tasks, refer to the section on Masking and Coding Tasks with ConfliBERT. The section on Computational Considerations and Benchmarks for ConfliBERT covers vital aspects of performance optimization.To explore the various adaptations of ConfliBERT, see the section on [ConfliBERT Variants]. At any point, you can refer to the References / Citations for further reading and information sources that have informed the development and use of ConfliBERT.
For a hands-on example of ConfliBERT in action, check out the Google Colab Demo.
This Google Colab document is a comprehensive guide for setting up and running experiments with the ConfliBERT language model, which is specifically tailored for analyzing political conflict and violence events. Here’s a breakdown of its functions:
Environment Setup:
torch, transformers, numpy,
scikit-learn, and pandas, which are essential
for machine learning tasks.simpletransformers, a library that
simplifies training and using transformer models.Cloning the ConfliBERT Source Code:
Importing Required Modules and Setting Arguments:
pandas for data
manipulation, and argparse for handling command line
arguments, are imported.args) is created to manage
various settings and configurations.Selecting a Dataset:
| Dataset | Task |
|---|---|
| 20news | binary |
| BBC_News | binary |
| IndiaPoliceEvents_doc | multilabel |
| IndiaPoliceEvents_sents | multilabel |
| cameo_class | multiclass |
| cameo_ner | ner |
| insightCrime | multilabel |
| re3d | ner |
| satp_relevant | multilabel |
For more detailed information about each dataset and to understand how to align them with your specific needs, please visit the ConfliBERT GitHub repository.
multilabel), the number of seeds for training, initial
seed value, number of epochs, batch size, and maximum sequence
length.ConfliBERT-scr-uncased and
bert-base-uncased), with their respective paths and
configurations.args
object.train_multi_seed function, passing the training,
evaluation, and test datasets along with the model configurations.Overall, this Colab document is an all-in-one toolkit for users looking to leverage the ConfliBERT model for their research or projects related to political events analysis.
ConfliBERT Hu et al. (2022) is a domain-specific pre-trained language model tailored for the analysis of political conflict and violence that was developed through a collaboration between conflict scholars and computer scientists. It was introduced in a paper titled “ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence,” which was presented at the North American Chapter of the Association for Computational Linguistics (NAACL) in 2022. ConfliBERT’s design and architecture cater to a wide range of interactions, from material and verbal conflicts to cooperative endeavors, encompassing events like protests, insurgencies, civil wars, and diplomatic disputes.
The study and monitoring of political violence and conflict have long been vital domains within the political science and policy communities. The traditional methods used by many event data systems to understanding these dynamics, such as manual coding and pattern-matching techniques, though valuable, have limitations in terms of scale and adaptability. Also, many of these legacy systems are dictionary-based, which makes them difficult to maintain and update Halterman et al. (2023). ConfliBERT helps to overcome these issues with its machine learning approach to data analysis.
ConfliBERT offers a promising avenue for advancing research in political science by enabling the efficient and comprehensive analysis of conflict processes. By bridging the expertise of political science and advanced natural language processing, it has the potential to redefine traditional methods, facilitating more nuanced and expansive research outcomes. It also represents a significant advancement in domain-specific language models for political conflict and violence, offering researchers and practitioners a tool that has been pre-trained on relevant data, thus ensuring better performance on domain-related tasks.
Domain-Specific Pre-training: While many language models are trained on general corpora, ConfliBERT’s training is rooted in domain-specific corpora. This focus allows for enhanced performance in performing classification tasks related to political violence, conflict, cooperation, and diplomacy.
Utility for Various Stakeholders: ConfliBERT is beneficial for academics, policymakers, and security analysts. It provides an efficient tool for monitoring and understanding the multifaceted dynamics of social unrest, political upheavals, and other conflict-related events on a global scale.
Automation and Efficiency: Traditional conflict analysis methods have their limitations in terms of scale and speed. ConfliBERT, with its automation capabilities, can parse vast datasets, significantly alleviating the challenges of manual annotations.
Broad Application Potential: Beyond mere data categorization, ConfliBERT can be employed for a diverse range of tasks related to conflict research, including event classification, entity recognition, relationship extraction, and data augmentation.
In the following sections, we will walk through illustrative examples of ConfliBERT’s capabilities in action. These examples are designed to help researchers and analysts better understand how ConfliBERT automates the coding of textual data into structured conflict and event data. Grasping these capabilities is essential for a myriad of applications such as real-time monitoring of political events, in-depth conflict analysis, and the accumulation of data for international relations and peace studies.
Make a Statement President Biden said that the U.S. alliance with Japan is stronger than ever.
Verbal Cooperation Saudi Arabia expressed intent to restore diplomatic relations with Iran.
Material Cooperation China provided humanitarian aid to Turkey.
Verbal Conflict President Zelenskyy accused Russia of war crimes.
Material Conflict Israeli forces attacked Hamas in Gaza City.
Platform and Requirements:
Model Versions:
These models are available on Huggingface and can be imported directly using its API.
Evaluation and Usage:
Evaluation Datasets:
Pre-Training Corpus:
Pre-Training Process:
run_mlm.py) from Huggingface. An example is provided for
pre-training on 8 GPUs, though parameters should be adapted based on the
user’s available resources.Citation:
NER
Classification of events
Masking and coding tasks
Named Entity Recognition (NER) is a fundamental task in the field of natural language processing (NLP) that involves identifying and classifying key information (entities) in text into predefined categories. These categories can include names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
ConfliBERT, like BERT, is inherently capable of recognizing and categorizing entities. It uses the context within which words appear to determine their meaning and classify them accordingly. The standard model can identify a variety of entities such as:
PERSON: People, including fictional persons.GPE: Geopolitical entities, like countries, cities, and
states.ORG: Organizations, including governmental, companies,
and agencies.ConfliBERT enhances BERT’s capabilities by being specifically fine-tuned on conflict and event data. This specialized focus enables it to be more effective in contexts related to political science, international relations, and conflict studies.
However, the scope of entities that can be recognized is not limited to the defaults. Users can fine-tune ConfliBERT on domain-specific corpora that contain unique entities of relevance to their research. For instance, one might train it to recognize military equipment, political parties, or specific event-related terminology.
To integrate more entities and classifications, researchers can proceed with the following steps:
Fine-tuning allows ConfliBERT to expand its understanding and recognition capabilities to align with specific research needs or domain-specific requirements.
President Biden PERSON said that the U.S. GPE alliance with Japan GPE is stronger than ever.
Saudi Arabia GPE expressed intent to restore diplomatic relations with Iran GPE.
China GPE provided humanitarian aid to Turkey GPE.
President Zelenskyy PERSON accused Russia GPE of war crimes.
Israeli forces ORG attacked Hamas ORG in Gaza City GPE.
Event data, in conflict research, refers to the systematic and chronological cataloging of political interactions, actions, and conflicts. It is instrumental in detailing when and where an incident happened, who was involved, and the nature of the event. The granularity of event data can range from large-scale political changes to minor altercations. Thus, the swift and accurate extraction of these data points from voluminous and diverse sources is paramount.
Here is how NER, when integrated with ConfliBERT, aids in the enrichment of event data:
Contextual Identification: With the specialized pre-training of ConfliBERT on conflict and political violence texts, the NER process becomes adept at distinguishing entities that are of significance in this domain. For instance, while general NLP models might identify ‘Aleppo’ merely as a location, ConfliBERT can contextualize it within the Syrian conflict, recognizing it as a key site of unrest.
Actor Recognition and Classification: Political events often involve a myriad of actors - from state agents and rebel groups to international entities. ConfliBERT’s NER can discern and categorize these actors, providing clarity on the roles they play in specific incidents. This facilitates a richer and more layered analysis of events.
Event Dynamics and Temporal Analysis: By associating entities with timestamps and specific actions, ConfliBERT can provide a chronological sequence of events. This temporal dimension is vital in understanding the progression of conflicts and predicting potential future escalations.
Enhancing Manual Coding: Manual coding of event data, a traditional method, can be significantly enhanced by automating the extraction process. ConfliBERT can quickly scan through vast amounts of data, flagging key entities and events, thereby reducing the manual labor and errors inherent in such a task.
Real-time Analysis: Given the swift nature of NER in ConfliBERT, real-time sources like news articles or social media feeds can be processed promptly. This ensures that event datasets remain up-to-date, capturing the dynamism of political conflicts.
Event Characterization: Beyond just identifying entities, NER in ConfliBERT can also help in characterizing the event itself. By recognizing and associating various entities and actions, the model can provide insights into the nature of the event, be it a peaceful protest, armed insurgency, or a diplomatic negotiation.
In essence, the synergy between ConfliBERT’s NER capabilities and event data extraction offers a transformative approach to conflict research. It streamlines the data gathering process, enhances the depth and breadth of analysis, and empowers researchers with a tool that resonates with the nuances of political violence and upheaval.
Basic NER terminology:
Before feeding the data to ConfliBERT (or any BERT-like model), one needs to ensure the text data is pre-processed:
Tokenization: Convert sentences into tokens. Depending on the language and context, tokenization might split words into subwords or characters.
Lowercasing: This step is optional and based on the pre-trained ConfliBERT model. Some BERT models are case-sensitive.
Special Tokens: BERT models usually expect [CLS] and [SEP] tokens to mark the beginning and end of a sentence, respectively.
For example:
[CLS] U.S. military chief General Colin Powell said ... [SEP]
Padding and Truncating: Make sure each input sequence has the same length by either padding short sequences or truncating long ones.
Entity Tags: Define a fixed set of entity tags,
such as B-S, I-S, B-T,
I-T, and O. This is crucial for the
classification layer’s output size.
Loss Function: Since NER is a multi-class classification problem, use a categorical cross-entropy loss.
Model Architecture: Depending on the version and customization of ConfliBERT, ensure the last layer is a dense layer with an output size equal to the number of unique entity tags.
The files provided seem to be in a CoNLL-like format, which is standard for NER tasks. A breakdown of the structure:
Example:
U.S. B-S
military I-S
...
. O
NATO B-S
...
. ONote: The input file’s structure needs to be consistent for effective model training and evaluation.
Depending on the specificities of the ConfliBERT model or the task requirements:
Users should prepare their data in a CoNLL-like format, ensure text pre-processing aligns with ConfliBERT’s requirements, and define a set of entity tags for classification. The more consistent and clean the input data, the better the model’s performance.
Based on the given example outputs, the sections for “Outputs from NER,” “Metrics for NER,” and “Evaluation of NER” are provided next.
Sentence: “For the first time in decades, there is at least the potential of an armed clash with America’s largest adversaries, Russia and China.”
Predictions: The named entity recognition model has
identified three entities in the given sentence: - decades:
Temporal Entity - America’s: Government -
Russia: Government - China: Government
Raw Outputs: There also is likely the raw logits or scores associated with each token for different potential named entity classes given with the outputs. For brevity, they are not all listed here, but they can be useful for diving deeper into model decisions or for model calibration.
Here is a general format you might encounter:
Precision: This is a measure of the accuracy provided that a specific class (or label) has been predicted. It is the number of true positives divided by the sum of true positives and false positives.
Recall: This is a measure of the ability of the model to find all the relevant cases within a dataset. It is the number of true positives divided by the sum of true positives and false negatives.
F1-Score: The F1 score is the harmonic mean of precision and recall. It provides a balance between the two. When it is closer to 1, it indicates better performance, and 0 indicates poorer performance.
Accuracy: This metric calculates the ratio of correctly predicted observation to the total observations.
(Note: For a complete evaluation, one would typically calculate these metrics for each entity type, as well as overall.)
The model seems to correctly identify the governmental entities
America’s, Russia, and China.
Additionally, the model successfully picked up on the temporal entity
decades.
However, the evaluation of the model’s performance would ideally require a much larger test dataset, encompassing a diverse range of sentences and contexts. Furthermore, an in-depth evaluation would involve comparing the model’s predictions against a ground truth or a labeled dataset to compute metrics like precision, recall, and F1-score.
One key aspect to consider in such models is their confidence scores (or probabilities) associated with predictions. From the raw outputs, we can extract these values to potentially set thresholds or make informed decisions.
Remember, while metrics provide a quantitative measure of performance, qualitative analysis (like manually examining where the model goes right or wrong) is invaluable, especially when deploying in critical applications.
Here is a detailed breakdown of the preprocessing and training steps for Named Entity Recognition (NER):
1_annotation_pipeline-12.21.ipynb2_Preprocess-Data-12.27.ipynb3_BiLSTM.ipynb4_transformer_reduced_bert.ipynb5_Baselines_reduced-2-step.ipynbpython create_input_files.pypython HAN_end2end.pyThe results section showcases the performance of various models on the task:
| Model | Accuracy (%) | Precision (%) | Recall (%) | F1 (%) | Exact Matching Ratio (%) |
|---|---|---|---|---|---|
| One vs Rest | 26.07 | 87.19 | 27.40 | 28.04 | 82.20 |
| Binary Relevance | 26.65 | 86.08 | 28.29 | 28.40 | 82.47 |
| Class Chain | 27.93 | 83.47 | 29.78 | 29.32 | 82.87 |
| Label Powerset | 28.86 | 85.53 | 30.51 | 30.05 | 83.14 |
| BiLSTM | 37.11 | 53.81 | 58.51 | 42.12 | 76.23 |
| HAN | 52.98 | 65.64 | 74.82 | 55.78 | 83.07 |
| BERT | 62.52 | 74.73 | 80.01 | 64.59 | 87.23 |
This comprehensive pipeline takes the data from raw, annotated form and processes it into a format that various machine learning and deep learning models can be trained on. The models’ performances are then evaluated and compared using the mentioned metrics.
Text classification is the process of assigning tags or categories to text according to its content. ConfliBERT can be trained to classify text into categories such as ‘Verbal Cooperation’, ‘Material Cooperation’, ‘Verbal Conflict’, and ‘Material Conflict’, based on the context and content of the input text.
Similar to entity recognition, classification categories can be tailored. Researchers can introduce additional categories like “Economic Sanctions”, “Legal Actions”, “Diplomatic Events”, etc., by fine-tuning ConfliBERT with appropriately tagged training data. This custom classification can help in creating more nuanced datasets that capture the complexity of international relations and conflict scenarios.
The fine-tuning process for expanding classification capabilities involves:
| Sentence | Make a Statement | Verbal Cooperation | Material Cooperation | Verbal Conflict | Material Conflict | Predicted Category |
|---|---|---|---|---|---|---|
| President Biden said that the U.S. alliance with Japan is stronger than ever. | 0.9 | 0.05 | 0.02 | 0.02 | 0.01 | Make a Statement |
| Saudi Arabia expressed intent to restore diplomatic relations with Iran. | 0.1 | 0.8 | 0.05 | 0.03 | 0.02 | Verbal Cooperation |
| China provided humanitarian aid to Turkey. | 0.05 | 0.1 | 0.8 | 0.03 | 0.02 | Material Cooperation |
| President Zelenskyy accused Russia of war crimes. | 0.02 | 0.03 | 0.05 | 0.85 | 0.05 | Verbal Conflict |
| Israeli forces attacked Hamas in Gaza City. | 0.01 | 0.02 | 0.03 | 0.1 | 0.84 | Material Conflict |
Please note, the logits (probabilities) here are illustrative and assume that the model is making predictions with high confidence and that each sentence strongly represents its respective category. The “Predicted Category” is determined by the highest logit score for each sentence. In real-world scenarios, the logits would be the actual output from a model like ConfliBERT after processing the sentences.
Here’s the revised section, incorporating HTML inline styling for color changes as per your examples:
This document provides an overview of classification metrics in the context of political event categorization using ConfliBERT, drawing upon foundational concepts in machine learning classification such as True Positives (TP), False Positives (FP), False Negatives (FN), and True Negatives (TN) as explained in the Google Machine Learning Crash Course (see “Machine Learning Crash Course: Classification: True/False Positive/Negative” (2021)). We will use a hypothetical example to illustrate these concepts clearly:
Metrics Explained:
True Positives (TP): Sentences where the Predicted Category matches the actual category (highest logit score).
False Positives (FP): Cases where the Predicted Category was chosen, but it is not the actual category.
False Negatives (FN): Cases where the actual category was correct, but the Predicted Category was different.
True Negatives (TN): All other cases where neither the predicted nor the actual category was chosen.
Hypothetical Example for Classification:
Consider an event: Israeli forces attacked Hamas in Gaza City. Let’s place this event in a 2x2 confusion matrix to understand potential outcomes from ConfliBERT’s classification:
| True Positive (TP) | False Negative (FN) |
|---|---|
| ConfliBERT accurately classifies the event as “Material Conflict” due to the active engagement and physical altercation reported. | ConfliBERT incorrectly classifies the event as a non-conflict category, such as “Verbal Cooperation,” missing the material nature of the conflict. |
| False Positive (FP) | True Negative (TN) |
|---|---|
| ConfliBERT incorrectly classifies a peaceful negotiation as “Material Conflict,” suggesting a physical altercation where there was none. | ConfliBERT correctly identifies that no material conflict occurred when Israeli forces did not engage with Hamas. |
Detailed Breakdown:
True Positive (TP): - Reality: Israeli forces engage in a physical altercation with Hamas in Gaza City. - ConfliBERT Says: “Material Conflict.” - Outcome: ConfliBERT’s classification aligns with the real-world event, providing accurate data for analysis.
False Positive (FP): - Reality: A diplomatic meeting between Israeli and Hamas representatives occurs without physical confrontation. - ConfliBERT Says: “Material Conflict.” - Outcome: ConfliBERT erroneously signals a conflict, potentially leading to misinformed analysis.
False Negative (FN): - Reality: Israeli forces attack Hamas in Gaza City. - ConfliBERT Says: “Verbal Cooperation” or another non-conflict category. - Outcome: Misclassification downplays the severity, potentially affecting response strategies.
True Negative (TN): - Reality: Israeli and Hamas representatives engage in peaceful talks. - ConfliBERT Says: “Verbal Cooperation” or an appropriate non-conflict category. - Outcome: ConfliBERT accurately reflects the peaceful nature of the event, aiding correct analysis and policy decisions.
Applying Metrics for Evaluation:
The subsequent sections will explore how to use these metrics—sensitivity, specificity, precision, and accuracy—to evaluate ConfliBERT’s performance in classifying complex political events. These metrics are essential for assessing ConfliBERT’s accuracy and fine-tuning its predictions to align with real-world observations.
Here is a deep dive into how ConfliBERT enhances classification in the context of conflict research:
Granular Event Categorization: While it is essential to identify an incident as a protest or a riot, the true value lies in discerning the nature of these events. ConfliBERT’s classification capability can distinguish between a peaceful protest, a violent uprising, or even a state-sanctioned crackdown. This granularity ensures a comprehensive understanding of the event’s nature.
Adaptive Learning with Domain-Specific Data: Given ConfliBERT’s training on conflict-related texts, it possesses a heightened sensitivity to the subtle nuances within conflict narratives. This domain-specific expertise translates to a more accurate and contextual classification, be it categorizing a skirmish as sectarian, ethnic, or political.
Temporal Classification: Conflicts evolve, and so do their narratives. ConfliBERT can classify events based on their temporal context, distinguishing between a long-standing civil war’s initial skirmishes and its climax battles or identifying the phases of diplomatic negotiations.
Automating Large-Scale Data Processing: Conflict research often grapples with voluminous data from diverse sources like news articles, social media, and firsthand accounts. Manual classification of such vast data is not only labor-intensive but also prone to inconsistencies. ConfliBERT automates this, ensuring uniformity and efficiency.
Real-time Classification for Dynamic Responses: In the rapidly changing landscape of political conflicts, timely responses are crucial. With its swift classification capabilities, ConfliBERT can process real-time data, ensuring that researchers and policymakers are equipped with classified insights as events transpire.
Supporting Cross-Referencing and Validation: By classifying data, ConfliBERT also aids in cross-referencing. For instance, if two sources provide conflicting narratives of an event, having them classified can help researchers quickly juxtapose and validate the information.
Assisting Predictive Analysis: Once events are classified systematically, it becomes feasible to perform predictive analysis. Recognizing patterns from past events can provide insights into potential future scenarios, aiding preemptive measures and strategies.
In summary, ConfliBERT’s classification capabilities not only structure the unorganized maze of conflict data but also elevate the depth and breadth of analysis. By providing categorized, context-aware insights, it becomes an invaluable asset for researchers, analysts, and policymakers navigating the intricate domain of conflict research.
For a different example, let’s create a hypothetical news report about a political demonstration that turned violent. This example will have more variance in the types of police actions and outcomes, and it will include different categorizations.
Original Text (Pre-Processing): “During a large political demonstration in the capital, clashes erupted between protesters and police. Police reportedly used water cannons and rubber bullets to control the situation. Several protesters were detained, and there were reports of injuries among both police and demonstrators. A local shop was vandalized during the chaos. The protest was eventually dispersed, and order was restored by the authorities.”
Processed Text (Post-Processing): The text is segmented into individual sentences and processed for analysis, with each sentence receiving specific categorizations based on the content.
| Processed Sentence | USE_OF_FORCE | DETENTION | INJURIES | VANDALISM | ORDER_RESTORED |
|---|---|---|---|---|---|
| During a large political demonstration in the capital, clashes erupted between protesters and police | 0 | 0 | 0 | 0 | 0 |
| Police reportedly used water cannons and rubber bullets to control the situation | 1 | 0 | 0 | 0 | 0 |
| Several protesters were detained | 0 | 1 | 0 | 0 | 0 |
| There were reports of injuries among both police and demonstrators | 0 | 0 | 1 | 0 | 0 |
| A local shop was vandalized during the chaos | 0 | 0 | 0 | 1 | 0 |
| The protest was eventually dispersed, and order was restored by the authorities | 0 | 0 | 0 | 0 | 1 |
In this table: - “USE_OF_FORCE” indicates if the sentence suggests the use of force by police. - “DETENTION” denotes if the sentence implies that protesters were detained. - “INJURIES” signifies if there were injuries reported among the police or demonstrators. - “VANDALISM” marks if there was property damage or vandalism mentioned. - “ORDER_RESTORED” is affirmative if the sentence indicates that order was restored by the authorities.
This structured approach to analyzing news text provides a clear understanding of the various aspects of the event, which is essential for conflict analysis and research.
To harness the capabilities of ConfliBERT for classification, one needs to be well-acquainted with the expected input format, necessary pre-processing steps, and customization options:
Text Pre-processing:
Customizing Classification with ConfliBERT:
There are four versions of ConfliBERT, each serving a specific purpose:
| Version | Definition |
|---|---|
| ConfliBERT-scr-uncased: | This version is pre-trained from scratch using a custom uncased vocabulary. This is the preferred version as it has been built from the ground up with a specific vocabulary set. |
| ConfliBERT-scr-cased: | This version is also pre-trained from scratch but uses a custom cased vocabulary. This means it differentiates between uppercase and lowercase letters. |
| ConfliBERT-cont-uncased: | This version is built by continually pre-training on top of the original BERT’s uncased vocabulary. |
| ConfliBERT-cont-cased: | This version is similar to the above but uses the original BERT’s cased vocabulary. |
Scratch versus Continuous Defined: 1. Scratch: This refers to building the model from scratch, meaning the model is trained without any prior knowledge. The vocabulary is also built from the base without any prior context. 2. Continuous: Continuous pre-training means the model is built on top of an existing model (in this case, BERT). This leverages the knowledge already present in BERT and fine-tunes it for a specific task.
When ConfliBERT provides a classification output, it organizes the results hierarchically and presents detailed information for clarity and to aid interpretation. Here is a deep dive into what each segment of the output signifies:
In essence, the output from ConfliBERT provides both a high-level and a detailed classification of the input sentence, complete with confidence scores, standardized representations, and a direct comparison to known correct answers for evaluation purposes. This hierarchical structure ensures a comprehensive understanding of the sentence’s context, entities involved, and the nature of their interaction.
(To be filled with information on how to use ConfliBERT with the Huggingface library, if applicable.)
(To be filled with relevant metrics used to evaluate the performance of ConfliBERT for classification, e.g., accuracy, F1 score, etc.)
(To be filled with details on how ConfliBERT’s classification performance was evaluated, including methodologies, datasets used, and results.)
[MASK] said that the U.S. alliance with Japan is stronger than ever.
Saudi Arabia expressed intent to restore diplomatic relations with [MASK].
[MASK] provided humanitarian aid to Turkey.
President Zelenskyy accused [MASK] of war crimes.
[MASK] forces attacked Hamas in Gaza City.
Language models like ConfliBERT employ a technique known as “masked language modeling” during the training process. Here is an in-depth breakdown of how masking functions within ConfliBERT:
1. The Principle of Masking:
Masking is the process where a certain percentage of input tokens are
replaced with a special [MASK] token. This technique
challenges the model to predict the masked word from its context. This
approach ensures that the model pays attention to both the left and the
right context of a word, fostering bidirectional understanding.
2. Benefits for Conflict Research:
In conflict research, data can often be inconsistent, fragmented, or
incomplete due to the sensitive nature of the information or the
challenges in data collection in conflict zones. Masking in ConfliBERT
can be pivotal as: - It trains the model to reconstruct missing or
obscured information. - It allows the model to predict contextually
relevant terms, crucial for accurate event classification and
relationship extraction.
3. Applications in Downstream Tasks:
The masked language modeling task equips ConfliBERT to handle various
downstream tasks such as named entity recognition, relationship
extraction, and event classification. The model’s ability to predict
masked entities can aid in: - Identifying relevant actors or groups in
conflict narratives. - Extracting the type of conflict event (e.g.,
bombing, armed assault). - Predicting relationships between entities in
a given text.
4. Enhancing Domain-Specific Understanding:
The specific vocabulary and context associated with the political
conflict domain make it challenging. When ConfliBERT undergoes training
with masking on conflict-specific datasets, it becomes better at
recognizing domain-specific terms, like “separatists”, and understanding
their relevance. This is crucial, as standard models might misinterpret
or fragment these terms.
5. Integration with Other Pre-training
Techniques:
While masking is essential, it is one of many pre-training techniques.
In conjunction with techniques like next-sentence-prediction, ConfliBERT
can develop a deeper understanding of text sequences and relationships,
enhancing its performance on complex conflict data.
In conclusion, masking is not just a technique but a cornerstone in the training regimen of ConfliBERT. It ensures that the model is well-equipped to tackle the intricate nuances and challenges associated with political conflict research. Through effective masking strategies, ConfliBERT becomes adept at providing comprehensive insights into conflict narratives, making it a valuable tool for conflict researchers and analysts.
To utilize ConfliBERT for masking and coding tasks, the input data must be structured and pre-processed according to specific guidelines.
Tokenization: The text should be tokenized into sub-words or words. The tokenization method will depend on the pre-trained model and tokenizer available for ConfliBERT.
Formatting: For the [MASK] task,
specific tokens or words in the sentence should be replaced with the
[MASK] token. The model will then predict the missing token
based on the context provided by the rest of the sentence.
Segmentation: If the input consists of multiple sentences, they should be divided into distinct segments. Each segment will be input separately into the model.
Padding: All inputs should be padded to have the same length for batching during model training or inference.
Predicted Tokens: The primary output from the masking task is the token predicted by ConfliBERT to replace the [MASK] token. The model will provide a probability distribution over the vocabulary, and the token with the highest probability is usually taken as the prediction.
Embeddings: ConfliBERT will also produce embeddings for each token in the input sequence. These embeddings can be used for downstream tasks or further analysis.
Coding Outputs: Depending on the coding task, ConfliBERT might classify a given input into predefined categories or produce other forms of structured output.
Accuracy: Measures the percentage of [MASK] tokens that the model predicted correctly.
Perplexity: Provides insight into how well the probability distribution predicted by the model aligns with the actual distribution of the [MASK] token.
F1-Score: Used mainly for coding tasks, it considers both precision and recall to provide a more holistic view of model performance.
Loss: The model’s objective function value, which it tries to minimize during training.
In-domain vs. Out-of-domain Evaluation: It is essential to evaluate the model’s performance both on data similar to its training data and on entirely different datasets.
Human Evaluation: Sometimes, multiple tokens can correctly fill a [MASK] token, so human evaluators can provide more nuanced feedback on the model’s predictions.
Ablation Studies: Evaluating the model’s performance by removing or modifying certain parts can help understand the importance of various components.
BERT: BERT is the pioneering model that introduced the [MASK] task. It is insightful to compare newer models like ConfliBERT with BERT to see the advancements.
RoBERTa: A variant of BERT, RoBERTa changes some key hyperparameters and training strategies, which can have different performances on the masking task.
When implementing BERT-based models such as ConfliBERT, one needs to consider the computational implications. While BERT benchmarks provide useful insights, ConfliBERT, as a derivative of BERT, might exhibit different behaviors. Here is a general breakdown based on the information provided:
BERT, and by extension models like ConfliBERT, are heavily parallelized models, which means they benefit significantly from multiple cores or GPUs. Using just a single core would be significantly slower and is not recommended for any sizable inference or training task. However, for very light tasks or experimentation, a single core could suffice, albeit with extended processing times.
Based on BERT benchmarks [https://vincentteyssier.medium.com/bert-inference-cost-performance-analysis-cpu-vs-gpu-b58a2420b2c8]:
The overarching pattern reveals that as the number of CPU cores increases, the processing time decreases. However, there is a point of diminishing returns: scaling from 16 to 32 vCPUs does not halve the processing time. Additionally, the vCPU usage decreases, indicating underutilization of resources with higher cores.
GPUs, especially those designed for deep learning tasks, like the NVIDIA Tesla series, provide significant speed-ups for BERT-like models due to their parallel processing capabilities.
For specialized architectures beyond Intel/AMD, the benchmarks would vary based on the specific architecture. For example, FPGAs or TPUs might offer different performance and cost metrics. However, TPUs, in particular, have been known to provide excellent performance for TensorFlow-based models, including BERT derivatives.
In conclusion, while BERT benchmarks give an idea of the computational requirements, users should perform their own benchmarks to ascertain the exact requirements for ConfliBERT. Always consider the scale of the task, available budget, and desired processing time when choosing the computational infrastructure.