Kashyap Popat


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Open Vocabulary Extreme Classification Using Generative Models
Daniel Simig | Fabio Petroni | Pouya Yanki | Kashyap Popat | Christina Du | Sebastian Riedel | Majid Yazdani
Findings of the Association for Computational Linguistics: ACL 2022

The extreme multi-label classification (XMC) task aims at tagging content with a subset of labels from an extremely large label set. The label vocabulary is typically defined in advance by domain experts and assumed to capture all necessary tags. However in real world scenarios this label set, although large, is often incomplete and experts frequently need to refine it. To develop systems that simplify this process, we introduce the task of open vocabulary XMC (OXMC): given a piece of content, predict a set of labels, some of which may be outside of the known tag set. Hence, in addition to not having training data for some labels–as is the case in zero-shot classification–models need to invent some labels on-thefly. We propose GROOV, a fine-tuned seq2seq model for OXMC that generates the set of labels as a flat sequence and is trained using a novel loss independent of predicted label order. We show the efficacy of the approach, experimenting with popular XMC datasets for which GROOV is able to predict meaningful labels outside the given vocabulary while performing on par with state-of-the-art solutions for known labels.

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Multilingual Autoregressive Entity Linking
Nicola De Cao | Ledell Wu | Kashyap Popat | Mikel Artetxe | Naman Goyal | Mikhail Plekhanov | Luke Zettlemoyer | Nicola Cancedda | Sebastian Riedel | Fabio Petroni
Transactions of the Association for Computational Linguistics, Volume 10

We present mGENRE, a sequence-to- sequence system for the Multilingual Entity Linking (MEL) problem—the task of resolving language-specific mentions to a multilingual Knowledge Base (KB). For a mention in a given language, mGENRE predicts the name of the target entity left-to-right, token-by-token in an autoregressive fashion. The autoregressive formulation allows us to effectively cross-encode mention string and entity names to capture more interactions than the standard dot product between mention and entity vectors. It also enables fast search within a large KB even for mentions that do not appear in mention tables and with no need for large-scale vector indices. While prior MEL works use a single representation for each entity, we match against entity names of as many languages as possible, which allows exploiting language connections between source input and target name. Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time. This leads to over 50% improvements in average accuracy. We show the efficacy of our approach through extensive evaluation including experiments on three popular MEL benchmarks where we establish new state-of-the-art results. Source code available at https://github.com/facebookresearch/GENRE.


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STANCY: Stance Classification Based on Consistency Cues
Kashyap Popat | Subhabrata Mukherjee | Andrew Yates | Gerhard Weikum
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users’ perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines.


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DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Kashyap Popat | Subhabrata Mukherjee | Andrew Yates | Gerhard Weikum
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.


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Making Headlines in Hindi: Automatic English to Hindi News Headline Translation
Aditya Joshi | Kashyap Popat | Shubham Gautam | Pushpak Bhattacharyya
The Companion Volume of the Proceedings of IJCNLP 2013: System Demonstrations

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The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis
Kashyap Popat | Balamurali A.R | Pushpak Bhattacharyya | Gholamreza Haffari
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Hybrid Stemmer for Gujarati
Pratikkumar Patel | Kashyap Popat | Pushpak Bhattacharyya
Proceedings of the 1st Workshop on South and Southeast Asian Natural Language Processing