United Arab Emirates

Causality detection and identification is centered on identifying semantic and cognitive connections in a sentence. 
 In this paper, we describe the effort of team LTRC for Causal News Corpus - Event Causality Shared Task 2022 at the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022). The shared task consisted of two subtasks: 1) identifying if a sentence contains a causality relation, and 2) identifying spans of text that correspond to cause, effect and signals. We fine-tuned transformer-based models with adapters for both subtasks. Our best-performing models obtained a binary F1 score of 0.853 on held-out data for subtask 1 and a macro F1 score of 0.032 on held-out data for subtask 2. Our approach is ranked third in subtask 1 and fourth in subtask 2. The paper describes our experiments, solutions, and analysis in detail.

EMNLP 2022

LTRC @ Causal News Corpus 2022: Extracting and Identifying Causal Elements using Adapters

casue and effect

adapters

information extraction

workshop paper

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

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Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Most of today's systems for socio-political event detection are text-based, while an increasing amount of information published on the web is multi-modal. We seek to bridge this gap by proposing a method that utilizes existing annotated unimodal data to perform event detection in another data modality, zero-shot. Specifically, we focus on protest detection in text and images, and show that a pretrained vision-and-language alignment model (CLIP) can be leveraged towards this end. In particular, our results suggest that annotated protest text data can act supplementarily for detecting protests in images, but significant transfer is demonstrated in the opposite direction as well.

Cross-modal Transfer Between Vision and Language for Protest Detection

In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus. We address the Causal Relation Identification (CRI) task by exploiting a set of simple yet complementary techniques for fine-tuning language models (LMs) on a few annotated examples (i.e., a few-shot configuration). We follow a prompt-based prediction approach for fine-tuning LMs in which the CRI task is treated as a masked language modeling problem (MLM). This approach allows LMs natively pre-trained on MLM tasks to directly generate textual responses to CRI-specific prompts. We compare the performance of this method against ensemble techniques trained on the entire dataset. Our best-performing submission was fine-tuned with only 256 instances per class, 15.7\% of the all available data, and yet obtained the second-best precision (0.82), third-best accuracy (0.82), and an F1-score (0.85) very close to what was reported by the winner team (0.86).

IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach

In this paper, we describe our shared task submissions for Subtask 2 in CASE-2022, Event Causality Identification with Casual News Corpus. The challenge focused on the automatic detection of all cause-effect-signal spans present in the sentence from news-media. We detect cause-effect-signal spans in a sentence using T5 — a pre-trained autoregressive language model. We iteratively identify all cause-effect-signal span triplets, always conditioning the prediction of the next triplet on the previously predicted ones. To predict the triplet itself, we consider different causal relationships such as cause→effect→signal. Each triplet component is generated via a language model conditioned on the sentence, the previous parts of the current triplet, and previously predicted triplets. Despite training on an extremely small dataset of 160 samples, our approach achieved competitive performance, being placed second in the competition. Furthermore, we show that assuming either cause→effect or effect→cause order achieves similar results.

IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model

The paper describes the work that has been submitted to the 5th workshop on Challenges and Applications of Automated Extraction of socio-political events from text (CASE 2022). The work is associated with Subtask 1 of Shared Task 3 that aims to detect causality in protest news corpus. The authors used different large language models with customized cross-entropy loss functions that exploit annotation information. The experiments showed that bert-based-uncased with refined cross-entropy outperformed the others, achieving a F1 score of 0.8501 on the Causal News Corpus dataset.

NoisyAnnot@ Causal News Corpus 2022: Causality Detection using Multiple Annotation Decisions

This paper details our participation in the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) workshop @ EMNLP 2022, where we take part in Subtask 1 of Shared Task 3 \textbackslash{}citep\{tan-etal-2022-event\}. 
We approach the given task of event causality detection by proposing a self-training pipeline that follows a teacher-student classifier method. 
More specifically, we initially train a teacher model on the true, original task data, and use that teacher model to self-label data to be used in the training of a separate student model for the final task prediction. 
We test how restricting the number of positive or negative self-labeled examples in the self-training process affects classification performance. 
Our final results show that using self-training produces a comprehensive performance improvement across all models and self-labeled training sets tested within the task of event causality sequence classification. On top of that, we find that self-training performance did not diminish even when restricting either positive/negative examples used in training.
Our code is be publicly available at \textbackslash{}hyperlink\{https://github.com/Gzhang-umich/1CademyTeamOfCASE\}\{https://github.com/Gzhang-umich/1CademyTeamOfCASE\}.

1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data

In this paper, we present our approach and empirical observations for Cause-Effect Signal Span Detection---Subtask 2 of Shared task 3 at CASE 2022. 
The shared task aims to extract the cause, effect, and signal spans from a given causal sentence.
We model the task as a reading comprehension (RC) problem and apply a token-level RC-based span prediction paradigm to the task as the baseline.
We explore different training objectives to fine-tune the model, as well as data augmentation (DA) tricks based on the language model (LM) for performance improvement.
Additionally, we propose an efficient beam-search post-processing strategy to due with the drawbacks of span detection to obtain a further performance gain.
Our approach achieves an average \$F\_1\$ score of 54.15 and ranks \textbackslash{}textbf\{\$1\^{}\{st\}\$\} in the CASE competition. Our code is available at \textbackslash{}url\{https://github.com/Gzhang-umich/1CademyTeamOfCASE\}.

1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector

Social and political researchers require robust event datasets to conduct data-driven analysis, an example being the need for trigger event datasets to analyze under what conditions and in what patterns certain trigger-type events increase the probability of mass killings. Fortunately, NLP and ML can be leveraged to create these robust datasets. In this paper we (i) outline a robust ML framework that prioritizes understandability through visualizations and generalizability through the ability to implement different ML algorithms, (ii) perform a comparative analysis of these ML tools within the framework for the coup trigger, (iii) leverage our ML framework along with a unique combination of NLP tools, such as NER and knowledge graphs, to produce a dataset for the the assassination trigger, and (iv) make this comprehensive, consolidated, and cohesive assassination dataset publicly available to provide temporal data for understanding political violence as well as training data for further socio-political research.

Hybrid Knowledge Engineering Leveraging a Robust ML Framework to Produce an Assassination Dataset

We report on the current status of an effort to produce political event data from unstructured text via a Transformer language model. Compelled by the current lack of publicly available and up-to-date event coding software, we seek to train a model that can produce structured political event records at the sentence level. Our approach differs from previous efforts in that we conceptualize this task as one of text-to-text sequence generation. We motivate this choice by outlining desirable properties of text generation models for the needs of event coding. To overcome the lack of sufficient training data, we also describe a method for generating synthetic text and event record pairs that we use to fit our model.

Political Event Coding as Text-to-Text Sequence Generation

We approach the classification problem as an entailment problem and apply zero-shot ranking to socio-political texts. Documents that are ranked at the top can be considered positively classified documents and this reduces the close reading time for the information extraction process. We use Transformer Language Models to get the entailment probabilities and investigate different types of queries. We find that DeBERTa achieves higher mean average precision scores than RoBERTa and when declarative form of the class label is used as a query, it outperforms dictionary definition of the class label. We show that one can reduce the close reading time by taking some percentage of the ranked documents that the percentage depends on how much recall they want to achieve. However, our findings also show that percentage of the documents that should be read increases as the topic gets broader.

Zero-Shot Ranking Socio-Political Texts with Transformer Language Models to Reduce Close Reading Time

Understanding causal relationship is an importance part of natural language processing. We address the causal information extraction problem with  different neural models built on top of pre-trained transformer-based language models for identifying Cause, Effect and Signal spans, from news data sets. We use the Causal News Corpus subtask 2 training data set to train span-based and sequence tagging models. Our span-based model based on pre-trained BERT base weights achieves an F1 score of 47.48 on the test set with an accuracy score of 36.87 and obtained 3rd place in the Causal News Corpus 2022 shared task.

LTRC @ Causal News Corpus 2022: Extracting and Identifying Causal Elements using Adapters

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Cross-modal Transfer Between Vision and Language for Protest Detection

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