United Arab Emirates

Research writing can be technical and diffi- cult to understand. Manual assignments of re- search areas and related faculties can be time- consuming and error-prone. Therefore, the first goal of this project is to build an unsu- pervised model to classify an unseen project proposal. We explored unsupervised learning methods such as K-means and topic models as well as a combination of Latent Dirichlet Annotation (LDA), Bidirectional Encoder Rep- resentations from Transformers (BERT) and K-means to cluster project proposals into dif- ferent categories. The second goal is to recom- mend papers for particular project proposals based on other similar publications. We can as- sume that the authors of the closest papers can be suitable supervisors for the research project. We investigated different features that can be used as numerical vector representation of doc- uments and apply cosine similarity method to find matching pairs of paper and proposal. We found out that TF-IDF gives the most accurate results.

EMNLP 2022

An Unsupervised Learning Approach for Categorising Research Proposals and Recommending Papers

recommendation system

text categorization

features extraction

topic modeling

unsupervised learning

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:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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To access this site you need to register. Please register [here](https://2022.emnlp.org/registration/).

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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.

The aim of this paper is present and regroup 70 research works that have been done for detecting Adverse Drug Events from social media. For each work, we focus on its description, its aim, its approach, the used models, the used datasets, its novelty and its limitation. We also add in the appendix part a table summarising all the studied works.

Detecting Adverse Drug Events from social media: A brief literature review

The gender bias present in the data on which language models are pre-trained gets reflected in the systems that use these models. The model's intrinsic gender bias shows an outdated and unequal view of women in our culture and encourages discrimination. Therefore, in order to establish more equitable systems and increase fairness, it is crucial to identify and mitigate the bias existing in these models. While there is a significant amount of work in this area in English, there is a dearth of research being done in other gendered and low resources languages, particularly the Indian languages. English is a non-gendered language, where it has genderless nouns. The methodologies for bias detection in English cannot be directly deployed in other gendered languages, where the syntax and semantics vary. In our paper, we measure gender bias associated with occupations in Hindi language models. Our major contributions in this paper are the construction of a novel corpus to evaluate occupational gender bias in Hindi and quantify this existing bias in these systems using a well-defined metric.

Evaluating Gender Bias in Pre-trained Indic Language Models

Despite recent advances in vision-and-language tasks, most progress is still focused on resource-rich languages such as English. Furthermore, widespread vision-and-language datasets directly adopt images representative of American or European cultures resulting in bias. Hence we introduce ParsVQA-Caps, the first benchmark in Persian for Visual Question Answering and Image Captioning tasks. We utilise two ways to collect datasets for each task, human-based and template-based for VQA and human-based and web-based for image captioning.

ParsVQA-Caps: A Benchmark for Visual Question Answering and Image Captioning in Persian

Social media platforms allow users to freely share their sentiments and opinions about is- sues and events or anything they feel like; however, they also make it easier to spread hate and abusive content. This paper introduces the HERDPhobia dataset, a manually annotated Twitter hate speech dataset against Fulani herders in Nigeria. The paper describes the data collection method, the annotation process, and the baseline experiment. We present a benchmark experiment using pre-trained languages models to classify the tweets as either hate speech or not hate speech, and the results were promising. We will release the dataset for further research in this direction.

HERDPhobia: A Dataset for Hate Speech Detection against Fulani Herdsmen in Nigeria

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.

Experiments on Generalizability of BERTopic on Short Text

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.

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Detecting Adverse Drug Events from social media: A brief literature review

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