Papers by Siddharth Dalmia
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit (2023.acl-demo)
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Brian Yan, Jiatong Shi, Yun Tang, Hirofumi Inaguma, Yifan Peng, Siddharth Dalmia, Peter Polák, Patrick Fernandes, Dan Berrebbi, Tomoki Hayashi, Xiaohui Zhang, Zhaoheng Ni, Moto Hira, Soumi Maiti, Juan Pino, Shinji Watanabe
| Challenge: | ESPnet-ST-v2 is a revamp of the open-source spoken language translation toolkit . it supports offline speech-to-text translation (ST), simultaneous speech- to-text (SST), and offline speech to-speech (S2ST) |
| Approach: | They propose to revamp the open-source ESPnet-ST toolkit to support offline speech-to-text translation, simultaneous speech- to-text and offline speech to-speech translation. |
| Outcome: | The updated version of ESPnet-ST supports offline speech-to-text translation (ST), simultaneous speech- to-text (SST), and offline speech to-speech translation (S2ST). |
Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion (P19-1)
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| Challenge: | Existing speech recognition systems are built at individual, isolated utterance level to make building systems computationally feasible. |
| Approach: | They propose to use text-based external word and/or sentence embeddings to integrate conversational context information into a single neural network model. |
| Outcome: | The proposed model outperforms standard end-to-end speech recognition models on the Switchboard conversational speech corpus and improves word error rate with better conversational-context representation. |
Epitran: Precision G2P for Many Languages (L18-1)
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| Challenge: | Epitran is a multilingual, multi-back-end system for grapheme-to-phoneme transduction . it supports 61 languages and is open source under an MIT license . |
| Approach: | Epitran is a multilingual back-end system for grapheme-to-phoneme transduction . it takes word tokens in the orthography of a language and outputs a phonemic representation . Epitran's efficacy has been demonstrated in multiple research projects . |
| Outcome: | Epitran is a multilingual, multi-backend system for grapheme-to-phoneme transduction . it supports 61 languages and is open source under MIT license . |
LOFT: Scalable and More Realistic Long-Context Evaluation (2025.findings-naacl)
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Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Séb Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu
| Challenge: | Long-context language models (LCLMs) can be used to perform tasks traditionally reliant on external tools like retrieval systems or databases. |
| Approach: | They propose a benchmark to evaluate LCLMs' performance on in-context retrieval and reasoning tasks using a set of tokens. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval and RAG systems on in-context retrieval tasks while still requiring prompting strategies. |
CTC Alignments Improve Autoregressive Translation (2023.eacl-main)
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Brian Yan, Siddharth Dalmia, Yosuke Higuchi, Graham Neubig, Florian Metze, Alan W Black, Shinji Watanabe
| Challenge: | Connectionist Temporal Classification (CTC) is widely used for automatic speech recognition (ASR) but lags behind attentional decoder approaches in terms of translation quality. |
| Approach: | They propose to use a CTC/attention framework to validate this hypothesis by modifying the Hybrid CTC-Attention model proposed for automatic speech recognition to support text-to-text translation (MT) and speech-totext translation. |
| Outcome: | The proposed model outperforms pure-attention baselines across six translation tasks. |
Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks (2021.naacl-main)
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| Challenge: | ESPnet framework exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks. |
| Approach: | They propose a framework that exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks. |
| Outcome: | The proposed framework outperforms the state-of-the-art on speech translation tasks by +6 and +3 BLEU on the two test sets of Fisher-CallHome and +4 BLUE on the English-German and English-French test sets. |
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering (2021.eacl-main)
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| Challenge: | Question-Answering (QA) systems are deployed in the real world . a lack of research attention has been devoted to studying the issues that arise when people use QA systems. |
| Approach: | They show that component components that precede an answering engine can introduce varied and considerable sources of error. |
| Outcome: | The proposed evaluations highlight the need for QA evaluation to expand to consider real-world use. |
Revisiting In-Context Learning with Long Context Language Models (2025.findings-acl)
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| Challenge: | In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. |
| Approach: | They revisited previous studies using in-context learning techniques . they found that using a data augmentation approach, they significantly improved ICL performance . |
| Outcome: | The proposed approach significantly improves ICL performance on 18 datasets spanning 4 tasks . the proposed approach does not improve performance over a simple random sample selection method . |
On Long-Tailed Phenomena in Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | State-of-the-art Neural Machine Translation models struggle with generating low-frequency tokens, tackling which remains a major challenge. |
| Approach: | They propose a loss function to better adapt model training to structural dependencies of conditional text generation by incorporating inductive biases of beam search into the training process. |
| Outcome: | The proposed method leads to significant gains over cross-entropy across different language pairs, especially on the generation of low-frequency words. |
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models (2022.findings-emnlp)
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| Challenge: | End-to-end spoken language understanding systems model sequence labeling as a sequence prediction task causing a divergence from its well-established token-level tagging formulation. |
| Approach: | They propose to model sequence labeling as a sequence prediction task . their systems explicitly separate the added complexity of recognizing spoken mentions from the NLU task of sequence labelling . |
| Outcome: | The proposed systems outperform both cascaded and direct models on a labeling task of named entity recognition across SLU benchmarks. |