Papers by Ashish Mittal
RECAST: Retrieval-Augmented Contextual ASR via Decoder-State Keyword Spotting (2025.findings-emnlp)
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| Challenge: | RECAST is a lightweight retrieval-augmented approach for contextual ASR . it repurposes decoder states of a pretrained ASR model to retrieve relevant keywords . |
| Approach: | RECAST is a retrieval-augmented approach that repurposes decoder states of a pretrained ASR model to retrieve relevant keywords without requiring audio exemplars. |
| Outcome: | RECAST outperforms full-list prompt biasing and strong phonetic/text baselines on 4,000 keywords across diverse domains. |
ODASim: Ordered, Distinctive and Absolute Semantic Similarity for Code Explanation Evaluation (2026.findings-acl)
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Prince Kumar, Vitobha Munigala, Jaydeep Sen, Ashish Mittal, Vishwajeet Kumar, Srikanth G. Tamilselvam
| Challenge: | Existing methods for code explanations fail to distinguish correct from partially or fully incorrect explanations and their similarity scores are poorly calibrated. |
| Approach: | They propose a model-agnostic graded fine-tuning framework that learns calibrated similarity representations between code and explanations to support fine-grained supervision and evaluation. |
| Outcome: | The proposed framework improves F1 score and ECE scores on two embedding models and reduces expected calibration error. |
Unified Semantic Parsing with Weak Supervision (P19-1)
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Priyanka Agrawal, Ayushi Dalmia, Parag Jain, Abhishek Bansal, Ashish Mittal, Karthik Sankaranarayanan
| Challenge: | Semantic parsing over multiple knowledge bases requires high-quality annotations of (utterance, program) pairs. |
| Approach: | They propose a framework to build a unified multi-domain enabled semantic parser with weak supervision. |
| Outcome: | The proposed model improves performance by 20% on the Overnight dataset. |
Power doesn’t reside in size: A Low Parameter Hybrid Language Model (HLM) for Sentiment Analysis in Code-mixed data (2025.emnlp-main)
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Pavan Sai Balaga, Nagasamudram Karthik, Challa Vishwanath, Raksha Sharma, Rudra Murthy, Ashish Mittal
| Challenge: | Code-mixed text presents significant challenges for machine learning due to interplay of distinct grammatical structures, effectively forming a hybrid language. |
| Approach: | They propose a Hybrid Language Model that combines a multilingual encoder and a lightweight decoder to achieve sentiment classification performance comparable to those of fine-tuned Large Language Models. |
| Outcome: | The proposed model outperforms models trained individually in sentiment detection tasks. |
Speech-enriched Memory for Inference-time Adaptation of ASR Models to Word Dictionaries (2023.emnlp-main)
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| Challenge: | Existing contextual biasing techniques require additional parameterization . state-of-the-art ASR systems often fail to recognize named entities or critical rare words . |
| Approach: | They propose an algorithm that uses nearest-neighbor matching to predict ASR models . a list of rare entities is indexed in a memory and then stored the best possible match . |
| Outcome: | The proposed algorithm improves the prediction of state-of-the-art ASR models on rare words . it prevents spurious matches by restricting to word-level matches . |
Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training (2022.findings-emnlp)
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| Challenge: | Existing DSS algorithms for RNN-T have a high cost and performance degradation. |
| Approach: | They propose a distributable DSS algorithm for RNN-T that can be used to train a subset of training data. |
| Outcome: | The proposed algorithm achieves between 3x to 6x speedup with only a small accuracy degradation even in settings where the training data is corrupted with noise. |
INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages (2025.findings-naacl)
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Abhishek Kumar Singh, Vishwajeet Kumar, Rudra Murthy, Jaydeep Sen, Ashish Mittal, Ganesh Ramakrishnan
| Challenge: | Large Language Models perform well on unseen tasks in English, but their abilities in non-English languages are less explored due to limited benchmarks and training data. |
| Approach: | They propose to release a large dataset for context-grounded question answering in 11 major Indian languages. |
| Outcome: | The Indic-QA Benchmark compared large datasets of large LLMs on extractive and abstractive tasks in 11 major Indian languages. |