United Arab Emirates

Topic modeling is a widely used analytical tool for evaluating large collections of textual data. One of the most popular topic modeling techniques is Latent Dirichlet Allocation (LDA). Although flexible and adaptive, it is not optimal for less conventional texts such as short texts from various domains. In this paper, we explore how the state-of-the-art BERTopic algorithm performs on short multi-domain text. We find that BERTopic HDBSCAN generalizes better than LDA in terms of topic coherence (0.091) and diversity (0.880). We further analyze the performance of the HDBSCAN clustering algorithm that BERTopic utilizes and find that suffers from predicting 74\% of the documents as outliers. We argue that this is a crucial, and yet overseen problem since it excludes the majority of the documents from the prediction. We propose to replace HDBSCAN with k-Means and show that similar performance can be achieved while controlling the number of outlier documents.

EMNLP 2022

Experiments on Generalizability of BERTopic on Short Text

bertopic

outliers

k-means

hdbscan

short text

multi-domain

topic modeling

## Welcome to EMNLP 2024! 
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EMNLP 2024

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## Welcome to EMNLP 2022!
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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.

Visual Question Answering is a multi-modal task under the consideration of both the Vision and Language communities. Present VQA models are limited to classification answers and cannot provide answers for reasoning questions. In this work, we introduce an encoder-decoder model using vision-and-language pretrained embedding, which delivers multi-word generated sentences as answers. We utilise LXMERT and VisualBERT embedding space with three different generative decoder heads, including RNNs, Attention RNNs and Transformers. Extensive experiments show competitive performance on the FSVQA dataset through qualitative and quantitative evaluation and a Human Error Analysis.

Generate Answer to Visual Questions with Pre-trained Vision-and-Language Embeddings

This paper presents a new multi-modal dataset for identifying hateful content on social media, consisting of 5,680 text-image pairs collected from Twitter, labeled across two labels. Experimental analysis of the presented dataset has shown that understanding both modalities is essential for detecting these techniques. It is confirmed in our experiments with several state-of-the-art multi-modal models. In future work, we plan to extend the dataset in size. We further plan to develop new multi-modal models tailored explicitly to hate-speech detection, aiming for a deeper understanding of the text and image relation. It would also be interesting to perform experiments in a direction that explores what social entities the given hate speech tweet targets.

A Multi-Modal Dataset for Hate Speech Detection on Social Media: Case-study of Russia-Ukraine Conflict

Event extraction involves the detection and extraction of both the event triggers and the corresponding arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this paper, we propose EventGraph, a joint framework for event extraction, which encodes events as graphs. We represent event triggers and arguments as nodes in a semantic graph. Event extraction therefore becomes a graph parsing problem, which provides the following advantages: 1) performing event detection and argument extraction jointly; 2) detecting and extracting multiple events from a piece of text; 3) capturing the complicated interaction between event arguments and triggers. Experimental results on ACE2005 show that our model is competitive to state-of-the-art systems and has substantially improved the results on argument extraction. Additionally, we create two new datasets from ACE2005 where we keep the entire text spans for event arguments, instead of just the head word(s). Our code and models will be released as open-source.

EventGraph: Event Extraction as Semantic Graph Parsing

We present our submission to Subtask 1 of the CASE-2022 Shared Task 3: Event Causality Identification with Causal News Corpus as part of the 5th Workshop on Challenges and Applications of Automated Extraction of Sociopolitical Events from Text (CASE 2022) (Tan et al., 2022a). The task focuses on causal event classification on the sentence level and involves differentiating between sentences that include a cause-effect relation and sentences that do not. We approached this as a binary text classification task and experimented with multiple training sets augmented with additional linguistic information. Our best model was generated by training roberta-base on a combination of data from both Subtasks 1 and 2 with the addition of named entity annotations. During the development phase we achieved a macro F1 of 0.8641 with this model on the development set provided by the task organizers. When testing the model on the final test data, we achieved a macro F1 of 0.8516.

NLP4ITF @ Causal News Corpus 2022: Leveraging Linguistic Information for Event Causality Classification

Event and argument role detection are frequently conceived as separate tasks. In this work we conceive both processes as one task
in a hybrid event detection approach. Its main component is based on automatic keyword extraction (AKE) using the self-attention mechanism of a BERT transformer model. 
As a bottleneck for AKE is defining the threshold of the attention values, we propose a novel method for automatic self-attention threshold
selection. It is fueled by core event information, or simply the verb and its arguments as the backbone of an event. These are outputted by a knowledge-based syntactic parser. In a second
step the event core is enriched with other semantically salient words provided by the transformer model. Furthermore, we propose an automatic self-attention layer and head selection
mechanism, by analyzing which self-attention cells in the BERT transformer contribute most to the hybrid event detection and which linguistic tasks they represent. This approach was integrated in a pipeline event extraction approach
and outperforms three state of the art multi-task event extraction methods.

A Hybrid Knowledge and Transformer-Based Model for Event Detection with Automatic Self-Attention Threshold, Layer and Head Selection

The success of sites such as ACLED and Our World in Data have demonstrated the massive utility of extracting events in structured formats from large volumes of textual data in the form
of news, social media, blogs and discussion forums.
Event extraction can provide a window into ongoing geopolitical crises and yield actionable intelligence. 

In this work, we cast socio-political event extraction as a machine reading comprehension (MRC) task. 
\% With the proliferation of large pretrained language models Machine Reading Comprehension (MRC) has emerged as a new paradigm for event extraction in recent times. 
In this approach, extraction of social-political actors and targets from a sentence is framed as an extractive question-answering problem conditioned on an event type. There are several advantages of using MRC for this task including the ability to leverage large pretrained multilingual language models and their ability to perform zero-shot extraction. 

Moreover, we find that the problem of long-range dependencies, i.e., large lexical distance between trigger and argument words and the difficulty of processing syntactically complex sentences plague MRC-based approaches.

To address this, we present a general approach to improve the performance of MRC-based event extraction by performing unsupervised sentence simplification guided by the MRC model itself. We evaluate our approach on the ICEWS geopolitical event extraction dataset, with specific attention to `Actor' and `Target' argument roles. We show how such context simplification can improve the performance of MRC-based event extraction by more than 5\textbackslash{}\% for actor extraction and more than 10\textbackslash{}\% for  target extraction.

Improving Zero-Shot Event Extraction via Sentence Simplification

Finding causal relations in texts has been a challenge since it requires methods ranging from defining event ontologies to developing proper algorithmic approaches. In this paper, we developed a framework which classifies whether a given sentence contains a causal event. As our approach, we exploited an external corpus that has causal labels to overcome the small size of the original corpus (Causal News Corpus) provided by task organizers. Further, we employed a data augmentation technique utilizing Part-Of-Speech (POS) based on our observation that some parts of speech are more (or less) relevant to causality. Our approach especially improved the recall of detecting causal events in sentences.

SNU-Causality Lab @ Causal News Corpus 2022: Detecting Causality by Data Augmentation via Part-of-Speech tagging

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.

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

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).

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Generate Answer to Visual Questions with Pre-trained Vision-and-Language Embeddings

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