Abstract

This paper describes our approach (IIITH) for SemEval-2021 Task 5: HaHackathon: Detecting and Rating Humor and Offense. Our results focus on two major objectives: (i) Effect of task adaptive pretraining on the performance of transformer based models (ii) How does lexical and hurtlex features help in quantifying humour and offense. In this paper, we provide a detailed description of our approach along with comparisions mentioned above.

Abstract

This paper describes our system (IREL) for 3C-Citation Context Classification shared task of the Scholarly Document Processing Workshop at NAACL 2021. We participated in both subtask A and subtask B. Our best system achieved a Macro F1 score of 0.26973 on the private leaderboard for subtask A and was ranked one. For subtask B our best system achieved a Macro F1 score of 0.59071 on the private leaderboard and was ranked two. We used similar models for both the subtasks with some minor changes, as discussed in this paper. Our best performing model for both the subtask was a finetuned SciBert model followed by a linear layer. This paper provides a detailed description of all the approaches we tried and their results.

Abstract

In many works, the problem of extractive summarisation has been framed as the problem of extracting the best summary from a given document. Many popular recent works aim to solve this by employing neural networks. And many of them are trained using a very limited scope, for example, a vast majority of the neural models are trained only using the best summary. Some also consider pruning useless summaries using other models. In this work, we show the problems that can arise when training neural models using such methods. We analyse those problems in some major milestones in Neural Extractive Summarisation. We also show and demonstrate ways to overcome them experimentally. Keywords: Extractive Summarisation · training bias

Abstract

The evolution of social media platforms have empowered everyone to access information easily. Social media users can easily share information with the rest of the world. This may sometimes encourage spread of fake news, which can result in undesirable consequences. In this work, we train models which can identify health news related to COVID-19 pandemic as real or fake. Our models achieve a high F1-score of 98.64%. Our models achieve second place on the leaderboard, tailing the first position with a very narrow margin 0.05% points.

Abstract

We propose a knowledge-based approach for extraction of Cause-Effect (CE) relations from biomedical text. Our approach is a combination of an unsupervised machine learning technique to discover causal triggers and a set of high-precision linguistic rules to identify cause/effect arguments of these causal triggers. We evaluate our approach using a corpus of 58,761 Leukaemia-related PubMed abstracts consisting of 568,528 sentences. We could extract 152,655 CE triplets from this corpus where each triplet consists of a cause phrase, an effect phrase and a causal trigger. As compared to the existing knowledge base - SemMedDB (Kilicoglu et al., 2012), the number of extractions are almost twice. Moreover, the proposed approach outperformed the existing technique SemRep (Rindflesch and Fiszman, 2003) on a dataset of 500 sentences.

Abstract

Every year publicly listed companies file financial reports to give insights about their activities. These reports are meant for shareholders or general public to evaluate the company’s health and decide whether to buy or sell stakes in the company. However, these annual financial reports tend to be long, and it is time-consuming to go through the reports for each company. We propose a Goal Guided Summarization technique through which the summary is extracted. The goal, in our case, is the decision to buy or sell company’s shares. We use hierarchical neural models for achieving this goal while extracting summaries. By the means of intrinsic and extrinsic evaluation we observe that the summaries extracted by our approach can model the decision of buying and selling shares better compared to summaries extracted by other summarization techniques as well as the complete document itself. We also observe that the summary extractor model can be used to construct stock portfolios which give better returns compared to major stock index.

Abstract

Social media platforms have democratized the publication process resulting into easy and viral propagation of information. However, spread of rumors via such media often results into undesired and extremely impactful political, economic, social, psychological and criminal consequences. Several manual as well as automated efforts have been undertaken in the past to solve this critical problem. Existing automated methods are text based, user credibility based or use signals from the tweet propagation tree. We aim at using the text, user, propagation tree and temporal information jointly for rumor detection on Twitter. This involves several challenges like how to handle text variations on Twitter, what signals from user profile could be useful, how to best encode the propagation tree information, and how to incorporate the temporal signal. Our novel architecture, T 3N (Text and Temporal Tree Network), leverages deep learning based architectures to encode text, user and tree information in a temporal-aware manner. Our extensive comparisons show that our proposed methods outperform the state-of-the-art techniques by ∼7 and ∼6 percent points respectively on two popular benchmark datasets, and also lead to better early detection results.

Abstract

Semantics play an important role when it comes to automated systems using text or language and it is different for different domains. In this paper, we tackle the FinSim 2020 shared task at IJCAIPRICAI 2020. The task deals with designing a semantic model which can automatically classify short phrases/terms from financial domain into the most relevant hypernym (or top-level) concept in an external ontology. We perform several experiments using different kinds of word and phrase level embeddings to solve the problem in an unsupervised manner. We also explore the use of a supplementary financial domain data; either to learn better concept representation or generate more training samples. We discuss both the positive and negative results that we observed while applying these approaches.

Abstract

Identifying adverse and hostile content on the web and more particularly, on social media, has become a problem of paramount interest in recent years. With their ever increasing popularity, fine-tuning of pretrained Transformer-based encoder models with a classifier head are gradually becoming the new baseline for natural language classification tasks. In our work, we explore the gains attributed to Task Adaptive Pretraining (TAPT) prior to fine-tuning of Transformer-based architectures. We specifically study two problems, namely,(a) Coarse binary classification of Hindi Tweets into Hostile or Not, and (b) Fine-grained multi-label classification of Tweets into four categories: hate, fake, offensive, and defamation. Building up on an architecture which takes emojis and segmented hashtags into consideration for classification, we are able to experimentally showcase the performance upgrades due to TAPT. Our system (with team name'iREL IIIT') ranked first in the'Hostile Post Detection in Hindi'shared task with an F1 score of 97.16% for coarse-grained detection and a weighted F1 score of 62.96% for fine-grained multi-label classification on the provided blind test corpora.

Abstract

Detecting and classifying instances of hate in social media text has been a problem of interest in NaturalLanguage Processing in the recent years. Our work leverages state of the art Transformer languagemodels to identify hate speech in a multilingual setting. Capturing the intent of a post or a comment onsocial media involves careful evaluation of the language style, semantic content and additional pointerssuch as hashtags and emojis. In this paper, we look at the problem of identifying whether a Twitterpost is hateful and offensive or not. We further discriminate the detected toxic content into one of thefollowing three classes: (a) Hate Speech (HATE), (b) Offensive (OFFN) and (c) Profane (PRFN). With apre-trained multilingual Transformer-based text encoder at the base, we are able to successfully identifyand classify hate speech from multiple languages. On the provided testing corpora, we achieve MacroF1 scores of 90.29, 81.87 and 75.40 for English, German and Hindi respectively while performing hatespeech detection and of 60.70, 53.28 and 49.74 during fine-grained classification. In our experiments, weshow the efficacy of Perspective API features for hate speech classification and the effects of exploitinga multilingual training scheme. A feature selection study is provided to illustrate impacts of specificfeatures upon the architecture’s classification head.