pwc-archive/papers-with-abstracts huggingface dataset:
MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities
Authors: Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, Jing Shao
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

Events are fundamental building blocks of real-world happenings. In this paper, we present a large-scale, multi-modal event knowledge graph named MMEKG. MMEKG unifies different modalities of knowledge via events, which complement and disambiguate each other.Specifically, MMEKG incorporates (i) over 990 thousand concept events with 644 relation types to cover most types of happenings, and (ii) over 863 million instance events connected through 934 million relations, which provide rich contextual information in texts and/or images. To collect billion-scale instance events and relations among them, we additionally develop an efficient yet effective pipeline for textual/visual knowledge extraction system. We also develop an induction strategy to create million-scale concept events and a schema organizing all events and relations in MMEKG. To this end, we also provide a pipeline enabling our system to seamlessly parse texts/images to event graphs and to retrieve multi-modal knowledge at both concept- and instance-levels.
QuickGraph: A Rapid Annotation Tool for Knowledge Graph Extraction from Technical Text
Authors: Tyler Bikaun, Michael Stewart, Wei Liu
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Clustering

Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators. Adoption of unsupervised techniques for automated annotation have thus become popular. However, these techniques rely heavily on dictionaries, gazetteers, and knowledge bases. While such resources are abundant for general domains, they are scarce for specialised technical domains. To tackle this challenge, we present QuickGraph, the first collaborative MT-IE annotation tool built with indirect weak supervision and clustering to maximise annotator productivity.QuickGraph’s main contribution is a set of novel features that enable knowledge graph extraction through rapid and consistent complex multi-task entity and relation annotation. In this paper, we discuss these key features and qualitatively compare QuickGraph to existing annotation tools.
A Gentle Introduction to Deep Nets and Opportunities for the Future
Authors: Kenneth Church, Valia Kordoni, Gary Marcus, Ernest Davis, Yanjun Ma, Zeyu Chen
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

The first half of this tutorial will make deep nets more accessible to a broader audience, following “Deep Nets for Poets” and “A Gentle Introduction to Fine-Tuning.” We will also introduce GFT (general fine tuning), a little language for fine tuning deep nets with short (one line) programs that are as easy to code as regression in statistics packages such as R using glm (general linear models). Based on the success of these methods on a number of benchmarks, one might come away with the impression that deep nets are all we need. However, we believe the glass is half-full: while there is much that can be done with deep nets, there is always more to do. The second half of this tutorial will discuss some of these opportunities.
Towards Reproducible Machine Learning Research in Natural Language Processing
Authors: Ana Lucic, Maurits Bleeker, Samarth Bhargav, Jessica Forde, Koustuv Sinha, Jesse Dodge, Sasha Luccioni, Robert Stojnic
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: BIG-bench Machine Learning

While recent progress in the field of ML has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility. Despite proposals such as the Reproducibility Checklist and reproducibility criteria at several major conferences, the reflex for carrying out research with reproducibility in mind is lacking in the broader ML community. We propose this tutorial as a gentle introduction to ensuring reproducible research in ML, with a specific emphasis on computational linguistics and NLP. We also provide a framework for using reproducibility as a teaching tool in university-level computer science programs.
Knowledge-Augmented Methods for Natural Language Processing
Authors: Chenguang Zhu, Yichong Xu, Xiang Ren, Bill Lin, Meng Jiang, Wenhao Yu
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Text Generation

Knowledge in natural language processing (NLP) has been a rising trend especially after the advent of large scale pre-trained models. NLP models with attention to knowledge can i) access unlimited amount of external information; ii) delegate the task of storing knowledge from its parameter space to knowledge sources; iii) obtain up-to-date information; iv) make prediction results more explainable via selected knowledge. In this tutorial, we will introduce the key steps in integrating knowledge into NLP, including knowledge grounding from text, knowledge representation and fusing. In addition, we will introduce recent state-of-the-art applications in fusing knowledge into language understanding, language generation and commonsense reasoning.
Non-Autoregressive Sequence Generation
Authors: Jiatao Gu, Xu Tan
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

Non-autoregressive sequence generation (NAR) attempts to generate the entire or partial output sequences in parallel to speed up the generation process and avoid potential issues (e.g., label bias, exposure bias) in autoregressive generation. While it has received much research attention and has been applied in many sequence generation tasks in natural language and speech, naive NAR models still face many challenges to close the performance gap between state-of-the-art autoregressive models because of a lack of modeling power. In this tutorial, we will provide a thorough introduction and review of non-autoregressive sequence generation, in four sections: 1) Background, which covers the motivation of NAR generation, the problem definition, the evaluation protocol, and the comparison with standard autoregressive generation approaches. 2) Method, which includes different aspects: model architecture, objective function, training data, learning paradigm, and additional inference tricks. 3) Application, which covers different tasks in text and speech generation, and some advanced topics in applications. 4) Conclusion, in which we describe several research challenges and discuss the potential future research directions. We hope this tutorial can serve both academic researchers and industry practitioners working on non-autoregressive sequence generation.
Learning with Limited Text Data
Authors: Diyi Yang, Ankur Parikh, Colin Raffel
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Data Augmentation, Structured Prediction, text-classification, Text Classification

Natural Language Processing (NLP) has achieved great progress in the past decade on the basis of neural models, which often make use of large amounts of labeled data to achieve state-of-the-art performance. The dependence on labeled data prevents NLP models from being applied to low-resource settings and languages because of the time, money, and expertise that is often required to label massive amounts of textual data. Consequently, the ability to learn with limited labeled data is crucial for deploying neural systems to real-world NLP applications. Recently, numerous approaches have been explored to alleviate the need for labeled data in NLP such as data augmentation and semi-supervised learning. This tutorial aims to provide a systematic and up-to-date overview of these methods in order to help researchers and practitioners understand the landscape of approaches and the challenges associated with learning from limited labeled data, an emerging topic in the computational linguistics community. We will consider applications to a wide variety of NLP tasks (including text classification, generation, and structured prediction) and will highlight current challenges and future directions.
Zero- and Few-Shot NLP with Pretrained Language Models
Authors: Iz Beltagy, Arman Cohan, Robert Logan IV, Sewon Min, Sameer Singh
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Few-Shot Learning

The ability to efficiently learn from little-to-no data is critical to applying NLP to tasks where data collection is costly or otherwise difficult. This is a challenging setting both academically and practically—particularly because training neutral models typically require large amount of labeled data. More recently, advances in pretraining on unlabelled data have brought up the potential of better zero-shot or few-shot learning (Devlin et al., 2019; Brown et al., 2020). In particular, over the past year, a great deal of research has been conducted to better learn from limited data using large-scale language models. In this tutorial, we aim at bringing interested NLP researchers up to speed about the recent and ongoing techniques for zero- and few-shot learning with pretrained language models. Additionally, our goal is to reveal new research opportunities to the audience, which will hopefully bring us closer to address existing challenges in this domain.
Vision-Language Pretraining: Current Trends and the Future
Authors: Aishwarya Agrawal, Damien Teney, Aida Nematzadeh
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Question Answering, Representation Learning, Visual Question Answering, Visual Question Answering (VQA)

In the last few years, there has been an increased interest in building multimodal (vision-language) models that are pretrained on larger but noisier datasets where the two modalities (e.g., image and text) loosely correspond to each other (e.g., Lu et al., 2019; Radford et al., 2021). Given a task (such as visual question answering), these models are then often fine-tuned on task-specific supervised datasets. (e.g., Lu et al., 2019; Chen et al.,2020; Tan and Bansal, 2019; Li et al., 2020a,b). In addition to the larger pretraining datasets, the transformer architecture (Vaswani et al., 2017) and in particular self-attention applied to two modalities are responsible for the impressive performance of the recent pretrained models on downstream tasks (Hendricks et al., 2021). In this tutorial, we focus on recent vision-language pretraining paradigms. Our goal is to first provide the background on image–language datasets, benchmarks, and modeling innovations before the multimodal pretraining area. Next we discuss the different family of models used for vision-language pretraining, highlighting their strengths and shortcomings. Finally, we discuss the limits of vision-language pretraining through statistical learning, and the need for alternative approaches such as causal representation learning.
Natural Language Processing for Multilingual Task-Oriented Dialogue
Authors: Evgeniia Razumovskaia, Goran Glavaš, Olga Majewska, Edoardo Ponti, Ivan Vulić
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

Recent advances in deep learning have also enabled fast progress in the research of task-oriented dialogue (ToD) systems. However, the majority of ToD systems are developed for English and merely a handful of other widely spoken languages, e.g., Chinese and German. This hugely limits the global reach and, consequently, transformative socioeconomic potential of such systems. In this tutorial, we will thus discuss and demonstrate the importance of (building) multilingual ToD systems, and then provide a systematic overview of current research gaps, challenges and initiatives related to multilingual ToD systems, with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. The tutorial will aim to provide answers or shed new light to the following questions: a) Why are multilingual dialogue systems so hard to build: what makes multilinguality for dialogue more challenging than for other NLP applications and tasks? b) What are the best existing methods and datasets for multilingual and cross-lingual (task-oriented) dialog systems? How are (multilingual) ToD systems usually evaluated? c) What are the promising future directions for multilingual ToD research: where can one draw inspiration from related NLP areas and tasks?
BERT-Based Sequence Labelling Approach for Dependency Parsing in Tamil
Authors: C S Ayush Kumar, Advaith Maharana, Srinath Murali, Premjith B, Soman Kp
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Dependency Parsing

Dependency parsing is a method for doing surface-level syntactic analysis on natural language texts. The scarcity of any viable tools for doing these tasks in Dravidian Languages has introduced a new line of research into these topics. This paper focuses on a novel approach that uses word-to-word dependency tagging using BERT models to improve the malt parser performance. We used Tamil, a morphologically rich and free word language. The individual words are tokenized using BERT models and the dependency relations are recognized using Machine Learning Algorithms. Oversampling algorithms such as SMOTE (Chawla et al., 2002) and ADASYN (He et al., 2008) are used to tackle data imbalance and consequently improve parsing results. The results obtained are used in the malt parser and this can be accustomed to further highlight that feature-based approaches can be used for such tasks.
A Dataset for Detecting Humor in Telugu Social Media Text
Authors: Sriphani Bellamkonda, Maithili Lohakare, Shaswat Patel
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Humor Detection

Increased use of online social media sites has given rise to tremendous amounts of user generated data. Social media sites have become a platform where users express and voice their opinions in a real-time environment. Social media sites such as Twitter limit the number of characters used to express a thought in a tweet, leading to increased use of creative, humorous and confusing language in order to convey the message. Due to this, automatic humor detection has become a difficult task, especially for low-resource languages such as the Dravidian languages. Humor detection has been a well studied area for resource rich languages due to the availability of rich and accurate data. In this paper, we have attempted to solve this issue by working on low-resource languages, such as, Telugu, a Dravidian language, by collecting and annotating Telugu tweets and performing automatic humor detection on the collected data. We experimented on the corpus using various transformer models such as Multilingual BERT, Multilingual DistillBERT and XLM-RoBERTa to establish a baseline classification system. We concluded that XLM-RoBERTa was the best-performing model and it achieved an F1-score of 0.82 with 81.5% accuracy.
TamilATIS: Dataset for Task-Oriented Dialog in Tamil
Authors: Ramaneswaran S, Sanchit Vijay, Kathiravan Srinivasan
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5

Task-Oriented Dialogue (TOD) systems allow users to accomplish tasks by giving directions to the system using natural language utterances. With the widespread adoption of conversational agents and chat platforms, TOD has become mainstream in NLP research today. However, developing TOD systems require massive amounts of data, and there has been limited work done for TOD in low-resource languages like Tamil. Towards this objective, we introduce TamilATIS - a TOD dataset for Tamil which contains 4874 utterances. We present a detailed account of the entire data collection and data annotation process. We train state-of-the-art NLU models and report their performances. The joint BERT model with XLM-Roberta as utterance encoder achieved the highest score with an intent accuracy of 96.26% and slot F1 of 94.01%.
DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil
Authors: Vasanth Palanikumar, Sean Benhur, Adeep Hande, Bharathi Raja Chakravarthi
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Abuse Detection, Data Augmentation, Transliteration

With the rise of social media and internet, thereis a necessity to provide an inclusive space andprevent the abusive topics against any gender,race or community. This paper describes thesystem submitted to the ACL-2022 shared taskon fine-grained abuse detection in Tamil. In ourapproach we transliterated code-mixed datasetas an augmentation technique to increase thesize of the data. Using this method we wereable to rank 3rd on the task with a 0.290 macroaverage F1 score and a 0.590 weighted F1score
UMUTeam@TamilNLP-ACL2022: Emotional Analysis in Tamil
Authors: José García-Díaz, Miguel Ángel Rodríguez García, Rafael Valencia-García
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Emotion Recognition, Sentence, Sentence Embeddings

This working notes summarises the participation of the UMUTeam on the TamilNLP (ACL 2022) shared task concerning emotion analysis in Tamil. We participated in the two multi-classification challenges proposed with a neural network that combines linguistic features with different feature sets based on contextual and non-contextual sentence embeddings. Our proposal achieved the 1st result for the second subtask, with an f1-score of 15.1% discerning among 30 different emotions. However, our results for the first subtask were not recorded in the official leader board. Accordingly, we report our results for this subtask with the validation split, reaching a macro f1-score of 32.360%.
UMUTeam@TamilNLP-ACL2022: Abusive Detection in Tamil using Linguistic Features and Transformers
Authors: José García-Díaz, Manuel Valencia-Garcia, Rafael Valencia-García
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Position, Sentence, Sentence Embeddings

Social media has become a dangerous place as bullies take advantage of the anonymity the Internet provides to target and intimidate vulnerable individuals and groups. In the past few years, the research community has focused on developing automatic classification tools for detecting hate-speech, its variants, and other types of abusive behaviour. However, these methods are still at an early stage in low-resource languages. With the aim of reducing this barrier, the TamilNLP shared task has proposed a multi-classification challenge for Tamil written in Tamil script and code-mixed to detect abusive comments and hope-speech. Our participation consists of a knowledge integration strategy that combines sentence embeddings from BERT, RoBERTa, FastText and a subset of language-independent linguistic features. We achieved our best result in code-mixed, reaching 3rd position with a macro-average f1-score of 35%.
JudithJeyafreedaAndrew@TamilNLP-ACL2022:CNN for Emotion Analysis in Tamil
Authors: Judith Jeyafreeda Andrew
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Emotion Recognition

Using technology for analysis of human emotion is a relatively nascent research area. There are several types of data where emotion recognition can be employed, such as - text, images, audio and video. In this paper, the focus is on emotion recognition in text data. Emotion recognition in text can be performed from both written comments and from conversations. In this paper, the dataset used for emotion recognition is a list of comments. While extensive research is being performed in this area, the language of the text plays a very important role. In this work, the focus is on the Dravidian language of Tamil. The language and its script demands an extensive pre-processing. The paper contributes to this by adapting various pre-processing methods to the Dravidian Language of Tamil. A CNN method has been adopted for the task at hand. The proposed method has achieved a comparable result.
MUCIC@TamilNLP-ACL2022: Abusive Comment Detection in Tamil Language using 1D Conv-LSTM
Authors: Fazlourrahman Balouchzahi, Anusha Gowda, Hosahalli Shashirekha, Grigori Sidorov
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Abusive Language

Abusive language content such as hate speech, profanity, and cyberbullying etc., which is common in online platforms is creating lot of problems to the users as well as policy makers. Hence, detection of such abusive language in user-generated online content has become increasingly important over the past few years. Online platforms strive hard to moderate the abusive content to reduce societal harm, comply with laws, and create a more inclusive environment for their users. In spite of various methods to automatically detect abusive languages in online platforms, the problem still persists. To address the automatic detection of abusive languages in online platforms, this paper describes the models submitted by our team - MUCIC to the shared task on “Abusive Comment Detection in Tamil-ACL 2022”. This shared task addresses the abusive comment detection in native Tamil script texts and code-mixed Tamil texts. To address this challenge, two models: i) n-gram-Multilayer Perceptron (n-gram-MLP) model utilizing MLP classifier fed with char-n gram features and ii) 1D Convolutional Long Short-Term Memory (1D Conv-LSTM) model, were submitted. The n-gram-MLP model fared well among these two models with weighted F1-scores of 0.560 and 0.430 for code-mixed Tamil and native Tamil script texts, respectively. This work may be reproduced using the code available in https://github.com/anushamdgowda/abusive-detection.
CEN-Tamil@DravidianLangTech-ACL2022: Abusive Comment detection in Tamil using TF-IDF and Random Kitchen Sink Algorithm
Authors: Prasanth S N, R Aswin Raj, Adhithan P, Premjith B, Soman Kp
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5

This paper describes the approach of team CEN-Tamil used for abusive comment detection in Tamil. This task aims to identify whether a given comment contains abusive comments. We used TF-IDF with char-wb analyzers with Random Kitchen Sink (RKS) algorithm to create feature vectors and the Support Vector Machine (SVM) classifier with polynomial kernel for classification. We used this method for both Tamil and Tamil-English datasets and secured first place with an f1-score of 0.32 and seventh place with an f1-score of 0.25, respectively. The code for our approach is shared in the GitHub repository.
NITK-IT_NLP@TamilNLP-ACL2022: Transformer based model for Toxic Span Identification in Tamil
Authors: Hariharan LekshmiAmmal, Manikandan Ravikiran, Anand Kumar Madasamy
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5

Toxic span identification in Tamil is a shared task that focuses on identifying harmful content, contributing to offensiveness. In this work, we have built a model that can efficiently identify the span of text contributing to offensive content. We have used various transformer-based models to develop the system, out of which the fine-tuned MuRIL model was able to achieve the best overall character F1-score of 0.4489.
GJG@TamilNLP-ACL2022: Emotion Analysis and Classification in Tamil using Transformers
Authors: Janvi Prasad, Gaurang Prasad, Gunavathi C
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Emotion Recognition, Multi-class Classification

This paper describes the systems built by our team for the “Emotion Analysis in Tamil” shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022. There were two multi-class classification sub-tasks as a part of this shared task. The dataset for sub-task A contained 11 types of emotions while sub-task B was more fine-grained with 31 emotions. We fine-tuned an XLM-RoBERTa and DeBERTA base model for each sub-task. For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.46 and the DeBERTa model achieved an accuracy of 0.45. We had the best classification performance out of 11 teams for sub-task A. For sub-task B, the XLM-RoBERTa model’s accuracy was 0.33 and the DeBERTa model had an accuracy of 0.26. We ranked 2nd out of 7 teams for sub-task B.