Papers by Suma Bhat
IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions (2023.emnlp-main)
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| Challenge: | Prior work on IE comprehension has focused on detecting idiomaticity, but this fails to account for IEs' non-compositionality. |
| Approach: | They construct a commonsense knowledge graph for figurative interpretations of IEs that can be used to convert PTLMs into knowledge models that encode and infer commonsensical knowledge related to IE use. |
| Outcome: | The proposed model can generalize to IEs unseen during training. |
Unified Representation for Non-compositional and Compositional Expressions (2023.findings-emnlp)
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| Challenge: | Existing representations of non-compositional language are based on BART, but they are not as accurate as the state-of-the-art IE representation model, GIEA. |
| Approach: | They propose a language model, PIER+, that builds on BART and can generate semantically meaningful and contextually appropriate representations for English potentially idiomatic expressions. |
| Outcome: | The proposed model achieves 33% higher homogeneity score on embedding clustering than BART, while sacrificing performance on NLU tasks (+/- 1% accuracy) |
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space (D19-1)
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| Challenge: | Existing approaches to reviewer-paper matching are less effective to deal with the vocabulary mismatch and partial topic overlap between the submission and reviewer. |
| Approach: | They propose to combine the common topic model and abstract topic vectors to model the topics common to the submission and the reviewer's profile while relying on abstract topic vectors. |
| Outcome: | The proposed model improves on the existing model on two datasets. |
Paraphrase Generation: A Survey of the State of the Art (2021.emnlp-main)
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| Challenge: | Using neural models, paraphrase generation research has shifted to neural methods . a recent study focused on paraphrases, which are used in language understanding tasks . |
| Approach: | They propose to use neural methods to generate fluent, diverse paraphrases from a sentence . they propose to combine large pretrained language models with other mechanisms to generate more advanced paraphrase generation models. |
| Outcome: | This paper examines various approaches to paraphrase generation with a main focus on neural methods. |
Idiomatic Expression Identification using Semantic Compatibility (2021.tacl-1)
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| Challenge: | Existing approaches to localize idiomatic expressions have limited views of their generalizability to new idioms. |
| Approach: | They propose a multi-stage neural architecture to detect whether a sentence has an idiomatic expression and localize it when it occurs in a figurative sense. |
| Outcome: | The proposed model achieves state-of-the-art on three of the largest datasets with idiomatic expressions of varied syntactic patterns and degrees of non-compositionality. |
Preposition Sense Disambiguation and Representation (D18-1)
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| Challenge: | Prepositions are highly polysemous and their variegated senses encode significant semantic information. |
| Approach: | They match each preposition’s context and their interplay to the geometry of the word vectors to the left and right of the preposition. |
| Outcome: | The proposed algorithm is comparable to and better than state-of-the-art on two benchmark datasets. |
GRUEN for Evaluating Linguistic Quality of Generated Text (2020.findings-emnlp)
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| Challenge: | Existing evaluation metrics focus on content selection, not linguistic quality . proposed GRUEN measures Grammaticality, non-redundancy, focUs, structure and coherence of generated text. |
| Approach: | They propose to use a BERT-based model and a class of syntactic, semantic, and contextual features to examine the system output. |
| Outcome: | Experiments show that the proposed metric correlates highly with human judgments. |
No Context Needed: Contextual Quandary In Idiomatic Reasoning With Pre-Trained Language Models (2024.naacl-long)
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| Challenge: | idiomatic expressions (IEs) are a non-compositional aspect of a text that makes it difficult for a model to comprehend . general purpose PTLMs are negatively affected by the context, as performance increases with its removal. |
| Approach: | They propose to use idiomatic expressions to infer additional meaning from IEs . they argue that only IE-aware models are suitable for idiom- matic reasoning tasks . |
| Outcome: | The proposed models can reason in the presence of idiomatic expressions, the authors show . they show that general purpose PTLMs are negatively affected by the context . |
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus (N19-1)
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| Challenge: | Existing methods for text style transfer have demonstrated considerable success, but a parallel corpus may not always be available for a transfer task. |
| Approach: | They propose a text style transfer model that uses an attention-based encoder-decoder to transfer a sentence from the source style to the target style. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two different style transfer tasks. |
Non-compositional Expression Generation Based on Curriculum Learning and Continual Learning (2023.findings-emnlp)
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| Challenge: | Non-compositional expressions are a classic ‘pain in the neck’ for NLP systems because of their non-composibility and limited data resources. |
| Approach: | They propose a dynamic curriculum learning framework which learns training examples from easy ones to harder ones but suffers from the forgetting problem. |
| Outcome: | The proposed framework improves on idiomatic expression generation and metaphor generation. |
Euphemistic Phrase Detection by Masked Language Model (2021.findings-emnlp)
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| Challenge: | euphemisms are ordinary-sounding words with a secret meaning that are used to conceal information . a primary motive of their use on social media is to evade content moderation efforts . |
| Approach: | They propose to use social media to detect euphemisms without human effort . they first perform phrase mining on a raw text corpus to extract quality phrases . then they use word embedding similarities to select a set of euphoristic phrase candidates . |
| Outcome: | The proposed algorithm shows 20-50% higher detection accuracies than baselines. |
Non-compositional Expression Generation and its Continual Learning (2024.findings-acl)
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| Challenge: | Recent work shows that pre-trained language models are limited in their ability to generate non-compositional expressions. |
| Approach: | They propose a mask-infilling task to examine non-compositional expressions in English . they compare large pre-trained language models and continual learning methods . |
| Outcome: | The proposed task aims to investigate the ability of pre-trained language models to generate non-compositional expressions in English and their continual learning. |
Document Similarity for Texts of Varying Lengths via Hidden Topics (P18-1)
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| Challenge: | Existing approaches to measure document similarity are inadequate for document pairs with non-comparable lengths, such as a long document and its summary. |
| Approach: | They propose a document matching approach to bridge the gap between long documents and their abstract information in a common space of hidden topics. |
| Outcome: | The proposed approach outperforms strong baselines on two matching tasks and incorporates domain knowledge to gain further performance improvement. |
CLASP: Cross-modal Alignment Using Pre-trained Unimodal Models (2024.findings-acl)
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| Challenge: | Recent advances in speech-text pretraining rely on parallel speech- text data . however, data accessibility is a challenge due to the limited data available. |
| Approach: | They propose a framework for jointly performing speech and text processing without parallel corpora during pre-training but only downstream. |
| Outcome: | The proposed framework extracts distinct representations for speech and text, aligning them effectively in a newly defined space using a multi-level contrastive learning mechanism. |
Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech (2021.findings-acl)
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| Challenge: | Off-the-shelf methods to generate hate speech are limited in that they generate repetitive and safe responses regardless of the hate speech. |
| Approach: | They propose a three-module pipeline approach to generate diverse and relevant counterspeech . they first generate various counterspeak candidates by a generative model, then filter ungrammatical ones using a BERT model . |
| Outcome: | The proposed pipeline generates diverse and relevant counterspeech responses on three datasets. |
Embedding Syntax and Semantics of Prepositions via Tensor Decomposition (N18-1)
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| Challenge: | Existing methods on preposition representation treat prepositions no different from content words (e.g., word2vec and GloVe). |
| Approach: | They propose to use word-triple counts to capture a preposition’s interaction with its attachment and complement and derive preposition embeddings via tensor decomposition on a large unlabeled corpus. |
| Outcome: | The proposed model is comparable to or better than the state-of-the-art on multiple standardized datasets. |
Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressions (2022.tacl-1)
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| Challenge: | Prior work has identified deficiencies in their contextualized representation stemming from the underlying compositional paradigm of representation. |
| Approach: | They propose to use an adapter as a lightweight non-compositional language expert trained on idiomatic sentences to build idiomity into BART. |
| Outcome: | The proposed approach improves idiomaticity over baselines and up to 25% higher sequence accuracy on idiom processing tasks. |
Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages (2026.acl-long)
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Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida, Irina Nikishina, Ashwath Rao B, Parameswari Krishnamurthy, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi, Nguyen Phan Gia Bao, Amir Hossein Yari, Hawau Olamide Toyin, Nurdaulet Mukhituly, Mena Attia, Besher Hassan, Ahmad Fathan Hidayatullah, Tatsuki Kuribayashi, Haonan Li, Suma Bhat, Fajri Koto
| Challenge: | idioms are a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation. |
| Approach: | They propose a multilingual idiom dataset that provides idiomatic expressions in both sentence-level and conversational contexts. |
| Outcome: | The proposed model performs well with low-resource idioms, but lacks contextual inference. |
CLCL: Non-compositional Expression Detection with Contrastive Learning and Curriculum Learning (2023.acl-long)
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| Challenge: | Non-compositional expressions are a substantial challenge for natural language processing systems, necessitating more intricate processing compared to general language tasks. |
| Approach: | They propose a dynamic curriculum learning framework specifically designed to take advantage of scarce available training data for modeling non-compositionality. |
| Outcome: | The proposed framework improves on idiom usage recognition and metaphor detection tasks. |