Papers by Diptesh Kanojia
FrameNet-assisted Noun Compound Interpretation (2021.findings-acl)
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| Challenge: | Existing methods for predicting semantic labels for noun compound interpretation are difficult. |
| Approach: | They propose to predict semantic labels in a continuous embedding space using FrameNet data. |
| Outcome: | The proposed method performs well on unseen labels, with 5% and 2% improvement over baselines for frame and FE prediction. |
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English (2025.findings-acl)
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| Challenge: | despite large language models showing bias against non-mainstream varieties, there are no labeled datasets for sentiment analysis of English. |
| Approach: | They propose a benchmark for sentiment and sarcasm classification for three varieties of English . they manually annotate the datasets with sentiment and the sarcasmatic labels . |
| Outcome: | The proposed benchmark is based on a web-based content from Google Place reviews and Reddit comments. |
Indian Language Wordnets and their Linkages with Princeton WordNet (L18-1)
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| Challenge: | Wordnets are rich lexico-semantic resources. Linked wordnets link similar concepts in wordnet of different languages. |
| Approach: | They propose to map 18 Indian wordnets linked with Princeton WordNet . they use expansion approach with Hindi Wordnet as pivot . |
| Outcome: | The proposed mappings of 18 Indian wordnets are based on Princeton WordNet . they show that availability of such resources will have a direct impact on NLP progress . |
What do Large Language Models Need for Machine Translation Evaluation? (2024.emnlp-main)
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Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia, Constantin Orasan, Tharindu Ranasinghe, Fred Blain
| Challenge: | Existing research shows that large language models can perform better in machine translation tasks. |
| Approach: | They propose to use large language models for machine translation evaluations . authors explore what translation information is needed for LLMs to evaluate MT quality . |
| Outcome: | The proposed model performs comparable to fine-tuned multilingual pre-trained models. |
Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages (2024.findings-emnlp)
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| Challenge: | Existing studies on multilingual automatic post-editing systems for low-resource Indo-Aryan languages have focused on different models for different language pairs. |
| Approach: | They propose to use a multilingual automatic post-editing system to improve machine translations for low-resource Indo-Aryan languages. |
| Outcome: | The proposed model outperforms English-Hindi and English-Marathi models by 2.5 and 2.39 TER points. |
Challenge Dataset of Cognates and False Friend Pairs from Indian Languages (2020.lrec-1)
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| Challenge: | Cognates are words that have a common etymological origin and can facilitate the Second Language Acquisition (SLA) however, they also pose a challenge to various NLP applications such as Machine Translation and Cross-lingual Sense Disambiguation. |
| Approach: | They create two cognate datasets for twelve Indian languages and use them to generate cognate sets. |
| Outcome: | The proposed datasets are curated using previously available baseline cognate detection approaches and evaluated with the help of lexicographers. |
YANMTT: Yet Another Neural Machine Translation Toolkit (2023.acl-demo)
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| Challenge: | Neural machine translation (NMT) is an end-to-end approach that provides stateof-the-art results for a variety of language pairs. |
| Approach: | They propose to build an open-source neural machine translation toolkit on top of HuggingFace's Transformers library and use it for pre-training and fine-tuning sequence-to-sequence models. |
| Outcome: | The proposed toolkit is built on top of the HuggingFace Transformers library and provides advanced features such as document/multi-source NMT, simultaneous NMT and mixtures-of-experts. |
“So You Think You’re Funny?”: Rating the Humour Quotient in Standup Comedy (2021.emnlp-main)
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| Challenge: | Existing datasets for humour classification are limited due to the subjectivity of the content and the multiple interpretations of the data. |
| Approach: | They propose to annotate a multi-modal humour-annotated dataset using stand-up comedy clips and compute a humor quotient using the audience's laughter. |
| Outcome: | The proposed scoring mechanism is validated by comparing with manual scoring methods and achieves an accuracy of 0.813 in terms of QWK. |
Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour (P18-1)
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Sandeep Mathias, Diptesh Kanojia, Kevin Patel, Samarth Agrawal, Abhijit Mishra, Pushpak Bhattacharyya
| Challenge: | Existing methods to predict text quality include estimating subjective aspects of text, like structure, clarity, etc. |
| Approach: | They propose to capture gaze behaviour to help predict text quality by reporting improvements obtained by adding gaze features to traditional textual features for score prediction. |
| Outcome: | The proposed model shows that capturing gaze behaviour improves the accuracy of score prediction when the reader has fully understood the text. |
Harnessing Abstractive Summarization for Fact-Checked Claim Detection (2022.coling-1)
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| Challenge: | Social media platforms are becoming battlegrounds for anti-social elements . fact-checking organizations cannot cope with the rapid dissemination of misinformation . a new workflow for fact- checking can be implemented to reduce human time for tasks with high cognition . |
| Approach: | They propose a workflow for detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries. |
| Outcome: | The proposed workflow achieves Recall@5 and MRR of 35% and 0.3, respectively. |
Refer to the Reference: Reference-focused Synthetic Automatic Post-Editing Data Generation (2025.coling-main)
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| Challenge: | Existing approaches to synthetic APE data generation use source (src) sentences in a parallel corpus to obtain translations (mt) through an MT system and treat corresponding reference (ref) sentences as post-edits (pe). |
| Approach: | They propose a reference-focused synthetic APE data generation technique that uses ‘ref’ instead of src’ sentences to obtain corrupted translations. |
| Outcome: | The proposed technique improves on English-German, English-Russian, English -Marathi, English and Hindi language pairs. |
Recommendation Chart of Domains for Cross-Domain Sentiment Analysis: Findings of A 20 Domain Study (2020.lrec-1)
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| Challenge: | Cross-domain sentiment analysis (CDSA) is a well-known problem in text analysis, but sufficient datasets may not be available for a domain to be trained. |
| Approach: | They propose to use 11 similarity metrics to facilitate cross-domain sentiment analysis to identify the best domains for CDSA for a given target domain. |
| Outcome: | The proposed approach performs better on 20 domain pairs and is validated by 11 similarity metrics. |
PLOD: An Abbreviation Detection Dataset for Scientific Documents (2022.lrec-1)
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| Challenge: | Existing datasets for abbreviation detection and extraction are limited. |
| Approach: | They propose to use a large-scale dataset for abbreviation detection and extraction that contains 160k+ segments automatically annotated with abbrevian and long forms. |
| Outcome: | The proposed dataset has an F1 score of 0.92 for abbreviations and 0.89 for detecting their corresponding long forms. |
Character-level Language Models for Abbreviation and Long-form Detection (2024.lrec-main)
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| Challenge: | Abbreviations and long forms are textual elements that are present in scientific communication . non-recognition of abbreviation and long form can lead to a negative impact on information retrieval . |
| Approach: | They propose to train and test language models for automatically identifying abbreviations and long forms . they use existing datasets annotated with abbrevations and their associated long forms to test them . |
| Outcome: | The proposed model can detect abbreviations and long forms on biomedical data . the proposed model improves on a previously untested dataset with biomedically-annotated datasets . |
Cognition-aware Cognate Detection (2021.eacl-main)
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Diptesh Kanojia, Prashant Sharma, Sayali Ghodekar, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
| Challenge: | Existing approaches to cognate detection use orthographic, phonetic and semantic similarity based features sets. |
| Approach: | They propose a method for enriching feature sets with cognitive features extracted from gaze behaviour data from human readers’ gaze behaviour. |
| Outcome: | The proposed method improves cognate detection performance by 10% and 12% over existing methods. |
HiNER: A large Hindi Named Entity Recognition Dataset (2022.lrec-1)
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Rudra Murthy, Pallab Bhattacharjee, Rahul Sharnagat, Jyotsana Khatri, Diptesh Kanojia, Pushpak Bhattacharyya
| Challenge: | Named Entity Recognition (NER) is a lowerlevel task that aims to provide class labels like Person, Location, Organisation, Time, and Number to words in free text. |
| Approach: | They propose to use a standard-abiding Hindi NER dataset to analyze the annotations of a class of naming entities in free text. |
| Outcome: | The proposed dataset achieves a weighted F1 score of 88.78 with all the tags and 92.22 when we collapse the tag-set. |
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour (2020.aacl-main)
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| Challenge: | Using gaze behaviour to solve automatic essay grading tasks is costly in terms of time and money. |
| Approach: | They propose to collect gaze behaviour from 48 essays and learn gaze behaviour for the rest of the essays using a multi-task learning framework. |
| Outcome: | The proposed approach achieves a statistically significant improvement over the state-of-the-art system for the essay sets where gaze data is available. |
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages (2020.coling-main)
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Diptesh Kanojia, Raj Dabre, Shubham Dewangan, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
| Challenge: | a study of 14 Indian languages shows that cognates can be detected by word embeddings . cognates are variants of the same lexical form across languages . |
| Approach: | They propose to use cross-lingual word embeddings to detect cognates among 14 Indian languages . they then evaluate the impact of their method on neural machine translation . |
| Outcome: | The proposed method improves on a dataset of 12 Indian languages . it also improves quality of the extracted cognates by up to 2.76 BLEU . |
Quality Estimation-Assisted Automatic Post-Editing (2023.findings-emnlp)
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| Challenge: | Existing APE and QE combination strategies have not shown significant performance gains in the field of automatic post-editing (APE). |
| Approach: | They propose to train a model on APE and QE tasks to improve the APE performance by using a multi-task learning methodology that treats both tasks as a 'bargaining game' they also investigate various existing combination strategies and show that their approach achieves state-of-the-art performance for a ‘distant’ language pair, viz., English-Marathi. |
| Outcome: | The proposed model improves on two different language pairs, viz., English-Marathi and English-German. |
Centrality-aware Product Retrieval and Ranking (2024.emnlp-industry)
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| Challenge: | Ambiguity and complexity of user queries often lead to mismatch between user’s intent and retrieved product titles or documents. |
| Approach: | They propose a user-intent centrality optimization approach which optimizes for the user intent in semantic product search. |
| Outcome: | The proposed approach improves product ranking efficiency for ambiguous queries and lexical terms with alphanumeric characters. |
Predict and Use: Harnessing Predicted Gaze to Improve Multimodal Sarcasm Detection (2023.emnlp-main)
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| Challenge: | sarcasm detection depends on content spoken, tonality, facial expressions, context, and personal traits like language proficiency and cognitive capabilities. |
| Approach: | They propose to use synthetic gaze data to improve sarcasm detection in conversational context . they collect gaze features for 20% of data instances and use them to predict gaze features . |
| Outcome: | The proposed model improves performance on a conversational dataset using gaze features . it achieves a gain of 6.6% points on the complete dataset with only predicted gaze features. |
A Multi-task Learning Framework for Quality Estimation (2023.findings-acl)
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Sourabh Deoghare, Paramveer Choudhary, Diptesh Kanojia, Tharindu Ranasinghe, Pushpak Bhattacharyya, Constantin Orăsan
| Challenge: | Conventional approaches to QE involve training separate models at different levels of granularity viz., word-level, sentence-level and document-level . |
| Approach: | They propose to train a single model for sentence-level and word-level QE tasks in a multi-task learning framework and compare them to baseline models. |
| Outcome: | The proposed model improves on the single-pair, multi-patch, and zero-shot settings. |
Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained Decoding for Automatic Post-Editing (2025.naacl-short)
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| Challenge: | Existing methods to improve automatic post-editing (APE) systems struggle with over-correction, despite the principle of minimal editing. |
| Approach: | They propose a method that incorporates word-level Quality Estimation (QE) information during the decoding process. |
| Outcome: | The proposed method improves on English-German, English-Hindi, and English-Marathi language pairs, with TER gains of 0.65, 1.86, and 1.44 points, respectively. |