Papers by Procheta Sen
Can Word Sense Distribution Detect Semantic Changes of Words? (2023.findings-emnlp)
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| Challenge: | Existing methods to detect semantic variations of words are not accurate for time-sensitive predictions. |
| Approach: | They propose to use pretrained static sense embeddings to annotate a word's occurrence with a sense id to compare its distributions. |
| Outcome: | The proposed method compares word sense distributions across two corpora to predict meaning change . the results show that pretrained LLMs can detect changes in words over time . |
FinBPM: A Framework for Portfolio Management-based Financial Investor Behavior Perception Model (2024.eacl-long)
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| Challenge: | a portfolio management framework based on reinforcement learning is needed to optimize stock price movements. |
| Approach: | They propose a framework that takes irrational investment into account when calculating portfolio weights . they use financial text to analyze intrinsic value information of companies and time series data . |
| Outcome: | The proposed framework gains 13.26% returns over state-of-the-art models while controlling for risk. |
CROWD: Certified Robustness via Weight Distribution for Smoothed Classifiers against Backdoor Attack (2024.findings-emnlp)
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| Challenge: | Existing studies on the certification of robustness of NLP models against backdoor attacks have focused on empirical defences against adversarial attacks without formal guarantees. |
| Approach: | They propose a model-agnostic mechanism for large-scale models that applies to complex model structures without the need for assessing model architecture or internal knowledge. |
| Outcome: | The proposed model-agnostic mechanism is tested on a diverse range of datasets and tasks, showing it can be used to mitigat backdoor triggers. |
A Counterfactual Explanation Framework for Retrieval Models (2026.findings-acl)
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| Challenge: | Existing literature on explainability of information retrieval has focused on illustrating the concept of relevance concerning a retrieval model. |
| Approach: | They propose to add terms to a document to improve its ranking to answer the question of which words played a role in not being favored by a retrieval model. |
| Outcome: | The proposed framework predicts counterfactuals for statistical and deep-learning models. |
Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction (N18-1)
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| Challenge: | Existing work on task extraction has focused on identifying tasks within a single session . but, we aim to identify tasks that span across multiple sessions. |
| Approach: | They propose to embed query words into query vectors to capture task semantics . they propose to use query vector embedding to predict whether a session is a part of a broader search task . |
| Outcome: | The proposed method improves task extraction efficiency over existing methods . it can predict whether a session is part of a broader complex search task . |
MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses (2025.emnlp-main)
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Tong Chen, Zimu Wang, Yiyi Miao, Haoran Luo, Sun Yuanfei, Wei Wang, Zhengyong Jiang, Procheta Sen, Jionglong Su
| Challenge: | Existing medical fact-checking datasets focus on human-generated content, leaving the verification of content generated by large language models (LLMs) relatively unexplored. |
| Approach: | They propose to use Chinese medical fact-checking datasets to verify LLM-generated medical content by combining in-context learning and fine-tuning. |
| Outcome: | The first evidence-based Chinese medical fact-checking dataset of LLM-generated medical content consists of 1,321 questions and 7,409 claims . |
Lexical Entrainment for Conversational Systems (2023.findings-emnlp)
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| Challenge: | Conversational agents are expected to possess human-like features such as lexical entrainment (LE). |
| Approach: | They propose a dataset and a measure for LE for conversational systems to explicitly integrate LE into conversational system. |
| Outcome: | The proposed dataset and a measure for LE for conversational systems address this human-like phenomenon. |
Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences (N19-1)
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| Challenge: | Existing word embedding algorithms make a strong assumption that words are semantically related only if they co-occur locally within a window of fixed size. |
| Approach: | They propose a graph-based word embedding method that relies on locality to capture the semantic association between words that co-occur frequently but non-locally within documents. |
| Outcome: | The proposed method outperforms word2vec and glove on a range of different tasks, such as predicting word-pair similarity, word analogy and concept categorization. |
Adaptive Retrieval-Augmented Generation for Conversational Systems (2025.findings-naacl)
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| Challenge: | Existing studies have shown the effectiveness of retrieving and augmenting external knowledge for informative responses. |
| Approach: | They propose to use a gating model to predict if a conversational system requires retrieval-augmented generation to generate high-quality responses with high confidence. |
| Outcome: | The proposed model can predict if a conversational system requires RAG to generate high-quality responses with high confidence. |