Niladri Chatterjee

Also published as: N. Chatterjee


Team LRL_NC at SemEval-2022 Task 4: Binary and Multi-label Classification of PCL using Fine-tuned Transformer-based Models
Kushagri Tandon | Niladri Chatterjee
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Patronizing and condescending language (PCL) can find its way into many mediums of public discourse. Presence of PCL in text can produce negative effects in the society. The challenge presented by the task emerges from the subtleties of PCL and various data dependent constraints. Hence, developing techniques to detect PCL in text, before it is propagated is vital. The aim of this paper is twofold, a) to present systems that can be used to classify a text as containing PCL or not, and b) to present systems that assign the different categories of PCL present in text. The proposed systems are primarily rooted in transformer-based pre-trained language models. Among the models submitted for Subtask 1, the best F1-Score of 0.5436 was achieved by a deep learning based ensemble model. This system secured the rank 29 in the official task ranking. For Subtask 2, the best macro-average F1-Score of 0.339 was achieved by an ensemble model combining transformer-based neural architecture with gradient boosting label-balanced classifiers. This system secured the rank 21 in the official task ranking. Among subsequently carried out experiments a variation in architecture of a system for Subtask 2 achieved a macro-average F1-Score of 0.3527.

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Summarization of Long Input Texts Using Multi-Layer Neural Network
Niladri Chatterjee | Aadyant Khatri | Raksha Agarwal
Proceedings of The Workshop on Automatic Summarization for Creative Writing

This paper describes the architecture of a novel Multi-Layer Long Text Summarizer (MLLTS) system proposed for the task of creative writing summarization. Typically, such writings are very long, often spanning over 100 pages. Summarizers available online are either not equipped enough to handle long texts, or even if they are able to generate the summary, the quality is poor. The proposed MLLTS system handles the difficulty by splitting the text into several parts. Each part is then subjected to different existing summarizers. A multilayer network is constructed by establishing linkages between the different parts. During training phases, several hyperparameters are fine-tuned. The system achieved very good ROUGE scores on the test data supplied for the contest.


LangResearchLab_NC at CMCL2021 Shared Task: Predicting Gaze Behaviour Using Linguistic Features and Tree Regressors
Raksha Agarwal | Niladri Chatterjee
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Analysis of gaze data behaviour has gained momentum in recent years for different NLP applications. The present paper aims at modelling gaze data behaviour of tokens in the context of a sentence. We have experimented with various Machine Learning Regression Algorithms on a feature space comprising the linguistic features of the target tokens for prediction of five Eye-Tracking features. CatBoost Regressor performed the best and achieved fourth position in terms of MAE based accuracy measurement for the ZuCo Dataset.

MTL782_IITD at CMCL 2021 Shared Task: Prediction of Eye-Tracking Features Using BERT Embeddings and Linguistic Features
Shivani Choudhary | Kushagri Tandon | Raksha Agarwal | Niladri Chatterjee
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Reading and comprehension are quintessentially cognitive tasks. Eye movement acts as a surrogate to understand which part of a sentence is critical to the process of comprehension. The aim of the shared task is to predict five eye-tracking features for a given word of the input sentence. We experimented with several models based on LGBM (Light Gradient Boosting Machine) Regression, ANN (Artificial Neural Network), and CNN (Convolutional Neural Network), using BERT embeddings and some combination of linguistic features. Our submission using CNN achieved an average MAE of 4.0639 and ranked 7th in the shared task. The average MAE was further lowered to 3.994 in post-task evaluation.

NARNIA at NLP4IF-2021: Identification of Misinformation in COVID-19 Tweets Using BERTweet
Ankit Kumar | Naman Jhunjhunwala | Raksha Agarwal | Niladri Chatterjee
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

The spread of COVID-19 has been accompanied with widespread misinformation on social media. In particular, Twitterverse has seen a huge increase in dissemination of distorted facts and figures. The present work aims at identifying tweets regarding COVID-19 which contains harmful and false information. We have experimented with a number of Deep Learning-based models, including different word embeddings, such as Glove, ELMo, among others. BERTweet model achieved the best overall F1-score of 0.881 and secured the third rank on the above task.

LangResearchLab NC at SemEval-2021 Task 1: Linguistic Feature Based Modelling for Lexical Complexity
Raksha Agarwal | Niladri Chatterjee
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

The present work aims at assigning a complexity score between 0 and 1 to a target word or phrase in a given sentence. For each Single Word Target, a Random Forest Regressor is trained on a feature set consisting of lexical, semantic, and syntactic information about the target. For each Multiword Target, a set of individual word features is taken along with single word complexities in the feature space. The system yielded the Pearson correlation of 0.7402 and 0.8244 on the test set for the Single and Multiword Targets, respectively.


LangResearchLab_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection
Raksha Agarwal | Ishaan Verma | Niladri Chatterjee
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

Identifying causal relationships in a text is essential for achieving comprehensive natural language understanding. The present work proposes a combination of features derived from pre-trained BERT with linguistic features for training a supervised classifier for the task of Causality Detection. The Linguistic features help to inject knowledge about the semantic and syntactic structure of the input sentences. Experiments on the FinCausal Shared Task1 datasets indicate that the combination of Linguistic features with BERT improves overall performance for causality detection. The proposed system achieves a weighted average F1 score of 0.952 on the post-evaluation dataset.


A Diagnostic Evaluation Approach Targeting MT Systems for Indian Languages
Renu Balyan | Sudip Kumar Naskar | Antonio Toral | Niladri Chatterjee
Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages


Context Resolution of Verb Particle Constructions for English to Hindi Translation
Niladri Chatterjee | Renu Balyan
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation


Word Alignment in English-Hindi Parallel Corpus Using Recency-Vector Approach: Some Studies
Niladri Chatterjee | Saumya Agrawal
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

Parsing Aligned Parallel Corpus by Projecting Syntactic Relations from Annotated Source Corpus
Shailly Goyal | Niladri Chatterjee
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions


Identification of divergence for English to Hindi EBMT
Deepa Gupta | Niladri Chatterjee
Proceedings of Machine Translation Summit IX: Papers

Divergence is a key aspect of translation between two languages. Divergence occurs when structurally similar sentences of the source language do not translate into sentences that are similar in structures in the target language. Divergence assumes special significance in the domain of Example-Based Machine Translation (EBMT). An EBMT system generates translation of a given sentence by retrieving similar past translation examples from its example base and then adapting them suitably to meet the current translation requirements. Divergence imposes a great challenge to the success of EBMT. The present work provides a technique for identification of divergence without going into the semantic details of the underlying sentences. This identification helps in partitioning the example database into divergence / non-divergence categories, which in turn should facilitate efficient retrieval and adaptation in an EBMT system.


Improving Automated Alignment in Multilingual Corpora
J.A. Campbell | N. Chatterjee | M. Manela | Alex Chengyu Fang
Proceedings of the 11th Pacific Asia Conference on Language, Information and Computation