Papers by Anuj Kumar
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020.coling-industry)
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Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White
| Challenge: | Natural language generation (NLG) is a critical component in conversational systems . Traditionally, NLG components have been deployed using template-based solutions . however, deployment of such model-based systems has been challenging due to high latency and data needs. |
| Approach: | They propose a family of techniques to deploy data-efficient neural solutions for NLG in conversational systems to production. |
| Outcome: | The proposed techniques achieve production quality with light-weight neural network models using fraction of the data needed otherwise. |
Conversational Semantic Parsing (2020.emnlp-main)
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Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Michael Haeger, Haoran Li, Yashar Mehdad, Veselin Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta
| Challenge: | Structured representations for task-oriented assistant systems are limited due to the limitations of the representation. |
| Approach: | They propose a semantic representation for task-oriented conversational systems that can represent co-reference and context carryover. |
| Outcome: | The proposed model improves the best results on ATIS, SNIPS, TOP and DSTC2 by up to 5 points for slot-carryover. |
MarathiEmoExplain: A Dataset for Sentiment, Emotion, and Explanation in Low-Resource Marathi (2025.findings-emnlp)
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| Challenge: | Marathi is the third most widely spoken language in India with over 83 million native speakers . available Marath datasets are limited to coarse sentiment labels and lack fine-grained emotional categorization or interpretability through explanations. |
| Approach: | They propose to annotate Marathi sentences labeled with sentiment, emotion and a corresponding natural language justification. |
| Outcome: | The proposed dataset provides a benchmark for future research in multilingual and explainable NLP. |
Seeing Beyond: Enhancing Visual Question Answering with Multi-Modal Retrieval (2025.coling-industry)
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| Challenge: | Multi-modal Large language models still suffer from model hallucination and lack of specific knowledge when answering challenging questions. |
| Approach: | They propose to use a multi-modal retrieval augmented generation method to integrate knowledge from all modalities into a model to enable alignment between query and knowledge. |
| Outcome: | The proposed method achieves significant performance improvement on the VQA dataset. |
Revisiting Evaluation of Question Answering Systems in Low-Resource Indic Languages: Bridging Human and Metric Alignment (2026.acl-short)
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| Challenge: | Evaluating Question Answering systems in low-resource Indic languages remains challenging due to the scarcity of annotated data and the lack of reliable evaluation metrics. |
| Approach: | They propose a language-based multi-aspect evaluation framework for question answering systems . the framework integrates semantic similarity, factual completeness, numerical accuracy and contextual relevance . |
| Outcome: | The proposed metric is evaluated across eight Indic-language QA tasks using multiple LLMs . Across all settings, it shows stronger agreement with human evaluation . |
MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines (2020.lrec-1)
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Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyang Gao, Adarsh Kumar, Anuj Goyal, Peter Ku, Dilek Hakkani-Tur
| Challenge: | MultiWOZ 2.0 has substantial noise in dialogue state annotations and dialogue utterances . follow-up work has augmented the original dataset with user dialogue acts . |
| Approach: | They propose to reannotate dialogue state and utterances based on original dataset . they then compare their results to other datasets to improve their models . |
| Outcome: | The proposed dataset improves on the noise in the dialogue state annotations and dialogue utterances. |
Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning (2026.eacl-industry)
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Minseok Kim, Jingxiang Chen, Seong-Gyun Leem, Yin Huang, Rashi Rungta, Zhicheng Ouyang, Haibin Wu, Surya Teja Appini, Ankur Bansal, Yang Bai, Yue Liu, Florian Metze, Ahmed A Aly, Anuj Kumar, Ariya Rastrow, Zhaojiang Lin
| Challenge: | Using paralinguistic cues is challenging for speech large language models, authors say . limited training data, annotation difficulty, and models exploiting lexical shortcuts are challenges . a recent study shows that modeling paralinguistic reasoning with multitask RL improves paralinguistics understanding . |
| Approach: | They propose multi-task reinforcement learning with chain-of-thought prompting that elicits explicit affective reasoning. |
| Outcome: | The proposed model improves paralinguistics understanding over baselines and strong proprietary models by 8-12% on Expresso, IEMOCAP, and RAVDESS. |
Memory Grounded Conversational Reasoning (D19-3)
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| Challenge: | Existing methods to retrieve and browse memories are keyword based searches or catalog based browsing systems. |
| Approach: | They propose a conversational system which engages the user through a multi-modal, multi-turn dialog over the user’s memories. |
| Outcome: | The proposed system can perform QA over memories and make suggestions to surface related events or facts from past memories to make conversations more engaging and natural. |
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)
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Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex L Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs. |
| Approach: | They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes. |
| Outcome: | The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning. |
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing (2021.eacl-main)
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| Challenge: | Code-switching (CS) is the alternation of languages within an utterance or conversation. |
| Approach: | They propose to use translation-and-align and augment with a generation model followed by match-and filter to improve CS generalizability of cross-lingual models when data for only one language is available. |
| Outcome: | The proposed models improve when only English data is available alongside zero or a few CS training instances. |
PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning (2025.emnlp-industry)
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Mohammad Kachuee, Teja Gollapudi, Minseok Kim, Yin Huang, Kai Sun, Xiao Yang, Jiaqi Wang, Nirav Shah, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing methods to improve factuality of large language models (LLMs) rely on human-engineered instructions. |
| Approach: | They propose a retrieval-augmented generation framework that trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages and instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without extensive human engineered instructions. |
| Outcome: | The proposed framework outperforms state-of-the-art solutions across 12 open-book RAG QA benchmarks and is being deployed in production. |
Proactive Assistant Dialogue Generation from Streaming Egocentric Videos (2025.emnlp-main)
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Yichi Zhang, Xin Luna Dong, Zhaojiang Lin, Andrea Madotto, Anuj Kumar, Babak Damavandi, Joyce Chai, Seungwhan Moon
| Challenge: | Recent advances in conversational AI have been substantial, but developing real-time tasks guidance systems remains a challenge. |
| Approach: | They propose a data curation pipeline that synthesizes dialogues from annotated egocentric videos and a suite of automatic evaluation metrics that validated through extensive human studies. |
| Outcome: | The proposed framework synthesizes dialogues from annotated egocentric videos and validates them through extensive human studies. |
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model (2024.emnlp-industry)
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Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, Kavya Srinet, Babak Damavandi, Anuj Kumar
| Challenge: | Prior work on LLMs focused on models that combine text and one other modality, such as image encoders or proprietary models that are not open sourced. |
| Approach: | They propose a unified model that reasons over diverse input modality signals and generates textual responses. |
| Outcome: | The proposed model performs better on multimodal tasks than industry-leading models . |
Semantic Parsing for Task Oriented Dialog using Hierarchical Representations (D18-1)
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| Challenge: | Existing work on task oriented dialog systems has limited expressive power to one intent per query and one slot label per token. |
| Approach: | They propose a hierarchical annotation scheme for semantic parsing that allows representation of compositional queries. |
| Outcome: | The proposed representation outperforms sequence-to-sequence approaches on a 44k annotated query dataset. |
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs (P19-1)
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| Challenge: | Existing models that use a large-scale knowledge graph to create a conversational reasoning model are domain-agnostic and scalable. |
| Approach: | They propose a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph to introduce engaging and contextually diverse entities and attributes. |
| Outcome: | The proposed model retrieves more natural responses than state-of-the-art models in both in-domain and cross-domain tasks. |
hinglishNorm - A Corpus of Hindi-English Code Mixed Sentences for Text Normalization (2020.coling-industry)
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| Challenge: | hinglishNorm is a human annotated corpus of Hindi-English code-mixed sentences for text normalization task. |
| Approach: | They propose to annotate sentences in Hindi-English code-mixed sentences using a human annotated normalized form. |
| Outcome: | The proposed corpus contains 13494 segments annotated for text normalization. |
Building Adaptive Acceptability Classifiers for Neural NLG (2021.emnlp-main)
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Soumya Batra, Shashank Jain, Peyman Heidari, Ankit Arun, Catharine Youngs, Xintong Li, Pinar Donmez, Shawn Mei, Shiunzu Kuo, Vikas Bhardwaj, Anuj Kumar, Michael White
| Challenge: | Existing approaches to generate synthetic data using simple sentence transformations and/or model-based techniques may not generate realistic error samples with respect to the NLG models. |
| Approach: | They propose a framework to train models to classify acceptability of responses generated by natural language generation models using a 2-stage approach . they use existing sentence transformations to generate samples that better resemble the output of the generation models. |
| Outcome: | The proposed approach outperforms existing techniques and can be used in few-shot settings using self-training. |