Abstract

Discovering new reaction pathways lies at the heart of drug discovery and chemical experimentation. A huge amount of drug reaction data lies in unannotated patent texts which are not machine readable. Reaction roles play an important role in analysing chemical pathways, and tracing chemicals through them, and while there is a vast body of chemical data available, the unavailability of reaction role annotated data is a blocker to effectively deploy deep learning methods for reaction discovery. This paper introduces a new dataset, WEAVE 2.0, obtained from chemical patents, along with full, manual, annotations of novel chemical reactions with reaction role information. We also provide baseline and state of the art models for chemical entity recognition from our raw dataset. Our dataset and associated models form the foundation of neural understanding of chemical reaction pathways via reaction roles.

Abstract

Sign language is a rich form of communication, uniquely conveying meaning through a combination of gestures, facial expressions, and body movements. Existing research in sign language generation has predominantly focused on text-to-sign pose generation, while speech-to-sign pose generation remains relatively underexplored. Speech-to-sign language generation models can facilitate effective communication between the deaf and hearing communities. In this paper, we propose an architecture that utilises prosodic information from speech audio and semantic context from text to generate sign pose sequences. In our approach, we adopt a multi-tasking strategy that involves an additional task of predicting Facial Action Units (FAUs). FAUs capture the intricate facial muscle movements that play a crucial role in conveying specific facial expressions during sign language generation.

Abstract

Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost, we apply manual editing to automatically translated data. We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed EnglishHindi language. X-RiSAWOZ has more than 18,000 human-verified dialogue utterances for each language, and unlike most multilingual prior work, is an end-to-end dataset for building fully-functioning agents. The many difficulties we encountered in creating X-RiSAWOZ led us to develop a toolset to accelerate the post-editing of a new language dataset after translation. This toolset improves machine translation with a hybrid entity alignment technique that combines neural with

Abstract

Code-mixing refers to the phenomenon of using two or more languages interchangeably within a speech or discourse context. This practice is particularly prevalent on social media platforms, and determining the embedded affects in a code-mixed sentence remains as a challenging problem. In this submission we describe our system for WASSA 2023 Shared Task on Emotion Detection in English-Urdu code-mixed text. In our system we implement a multiclass emotion detection model with label space of 11 emotions. Samples are code-mixed English-Urdu text, where Urdu is written in romanised form. Our submission is limited to one of the subtasks - Multi Class classification and we leverage transformer-based Multilingual Large Language Models (MLLMs), XLM-RoBERTa and Indic-BERT. We fine-tune MLLMs on the released data splits, with and without pre-processing steps (translation to english), for classifying texts into the appropriate emotion category. Our methods did not surpass the baseline, and our submission is ranked sixth overall.

Abstract

In this paper, we present our team’s involvement in Task 6: LegalEval: Understanding Legal Texts. The task comprised three subtasks, and we focus on subtask A: Rhetorical Roles prediction. Our approach included experimenting with pre-trained embeddings and refining them with statistical and neural classifiers. We provide a thorough examination ofour experiments, solutions, and analysis, culminating in our best-performing model and current progress. We achieved a micro F1 score of 0.6133 on the test data using fine-tuned LegalBERT embeddings.

Abstract

This paper introduces PMIndiaSum, a new multilingual and massively parallel headline summarization corpus focused on languages in India. Our corpus covers four language families, 14 languages, and the largest to date, 196 language pairs. It provides a testing ground for all cross-lingual pairs. We detail our workflow to construct the corpus, including data acquisition, processing, and quality assurance. Furthermore, we publish benchmarks for monolingual, cross-lingual, and multilingual summarization by fine-tuning, prompting, as well as translate-and-summarize. Experimental results confirm the crucial role of our data in aiding the summarization of Indian texts. Our dataset is publicly available and can be freely modified and re-distributed.

Abstract

In this paper, we have worked on interpretability, trust, and understanding of the decisions made by models in the form of classification tasks. The task is divided into 3 subtasks. The first task consists of determining Binary Sexism Detection. The second task describes the Category of Sexism. The third task describes a more Fine-grained Category of Sexism. Our work explores solving these tasks as a classification problem by fine-tuning transformerbased architecture. We have performed several experiments with our architecture, including combining multiple transformers, using domain adaptive pretraining on the unlabelled dataset provided by Reddit and Gab, Joint learning, and taking different layers of transformers as input to a classification head. Our system (with team name ‘Attention’) was able to achieve a macro F1 score of 0.839 for task A, 0.5835 macro F1 score for task B and 0.3356 macro F1 score for task C at the Codalab SemEval Competition. Later we improved the accuracy of Task B to 0.6228 and Task C to 0.3693 in the test set.

Abstract

The tourism industry is important for the benefits it brings and due to its role as a commercial activity that creates demand and growth for many more industries. Yet there is not much work on data science problems in tourism. Unfortunately, there is not even a standard benchmark for evaluation of tourismspecific data science tasks and models. In this paper, we propose a benchmark, TOURISMNLG, of five natural language generation (NLG) tasks for the tourism domain and release corresponding datasets with standard train, validation and test splits1 . Further, previously proposed data science solutions for tourism problems do not leverage the recent benefits of transfer learning. Hence, we also contribute the first rigorously pretrained mT5 and mBART model checkpoints for the tourism domain. The models have been pretrained on four tourism-specific datasets covering different aspects of tourism. Using these models, we present initial baseline results on the benchmark tasks. We hope that the dataset will promote active research for natural language generation for travel and tourism. Keywords: NLG for Tourism · Long QA · Blog-Title Generation · Forum-Title Generation · Paragraph Generation · Short QA

Abstract

The ILSUM shared task focuses on text summarization for two major Indian languages- Hindi and Gujarati, along with English. In this task, we experiment with various pretrained sequence-to-sequence models to find out the best model for each of the languages. We present a detailed overview of the models and our approaches in this paper. We secure the first rank across all three sub-tasks (English, Hindi and Gujarati). This paper also extensively analyzes the impact of k-fold cross-validation while experimenting with limited data size, and we also perform various experiments with a combination of the original and a filtered version of the data to determine the efficacy of the pretrained models.

Abstract

Open-domain question qnswering (OpenQA) involves a retriever for selecting relevant passages from large text corpora (e.g. Wikipedia) and a reading comprehension (RC) model for extracting answers from these retrieved passages. The retrieved passages are often noisy. Since OpenQA relies heavily on efficient passages for better answer prediction, many passage ranker models have been pro- posed to filter out noisy passages. However, their performance is limited because their ranker model scores each passage separately by modelling only the relationship between query and passage. Thus, they could not capture local context information. Their ranker model also ignored the rich initial rank of passages ranked by a search engine. This paper presents a Passage Ranker model that captures local-context information through cross-passage interac- tion. Our ranker model integrates initial ranking and uses modified attention in the cross-passage interaction to compute a better confi- dence score for each passage. Moreover, we integrate SRL into our passage reader and train it on proposed sampled data. Our semantic reader can absorb contextual semantics. Experimental results on four public OpenQA datasets show that o