Somak Aditya


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Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance
Karthikeyan K | Aalok Sathe | Somak Aditya | Monojit Choudhury
Proceedings of the 1st Workshop on Multilingual Representation Learning

Multilingual language models achieve impressive zero-shot accuracies in many languages in complex tasks such as Natural Language Inference (NLI). Examples in NLI (and equivalent complex tasks) often pertain to various types of sub-tasks, requiring different kinds of reasoning. Certain types of reasoning have proven to be more difficult to learn in a monolingual context, and in the crosslingual context, similar observations may shed light on zero-shot transfer efficiency and few-shot sample selection. Hence, to investigate the effects of types of reasoning on transfer performance, we propose a category-annotated multilingual NLI dataset and discuss the challenges to scale monolingual annotations to multiple languages. We statistically observe interesting effects that the confluence of reasoning types and language similarities have on transfer performance.


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TaxiNLI: Taking a Ride up the NLU Hill
Pratik Joshi | Somak Aditya | Aalok Sathe | Monojit Choudhury
Proceedings of the 24th Conference on Computational Natural Language Learning

Pre-trained Transformer-based neural architectures have consistently achieved state-of-the-art performance in the Natural Language Inference (NLI) task. Since NLI examples encompass a variety of linguistic, logical, and reasoning phenomena, it remains unclear as to which specific concepts are learnt by the trained systems and where they can achieve strong generalization. To investigate this question, we propose a taxonomic hierarchy of categories that are relevant for the NLI task. We introduce TaxiNLI, a new dataset, that has 10k examples from the MNLI dataset with these taxonomic labels. Through various experiments on TaxiNLI, we observe that whereas for certain taxonomic categories SOTA neural models have achieved near perfect accuracies—a large jump over the previous models—some categories still remain difficult. Our work adds to the growing body of literature that shows the gaps in the current NLI systems and datasets through a systematic presentation and analysis of reasoning categories.


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Identifying Various Kinds of Event Mentions in K-Parser Output
Arpit Sharma | Nguyen Vo | Somak Aditya | Chitta Baral
Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation

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Recognizing Social Constructs from Textual Conversation
Somak Aditya | Chitta Baral | Nguyen Ha Vo | Joohyung Lee | Jieping Ye | Zaw Naung | Barry Lumpkin | Jenny Hastings | Richard Scherl | Dawn M. Sweet | Daniela Inclezan
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies