Papers with MMS
Progressive Visual Refinement for Multi-modal Summarization (2026.eacl-short)
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| Challenge: | Multi-modal summarization (MMS) is a critical research area driven by the proliferation of multimedia content. |
| Approach: | They propose a patch-refined visual information network to exploit multimodal information . they propose combining visual information with textual information to generate concise summaries . |
| Outcome: | Extensive experiments on two public MMS datasets show the superiority of the proposed model. |
Language Model Decoding as Likelihood–Utility Alignment (2023.findings-eacl)
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Martin Josifoski, Maxime Peyrard, Frano Rajič, Jiheng Wei, Debjit Paul, Valentin Hartmann, Barun Patra, Vishrav Chaudhary, Emre Kiciman, Boi Faltings
| Challenge: | Existing studies only compare decoding algorithms in narrow scenarios, and their findings do not generalize across tasks. |
| Approach: | They propose a taxonomy of misalignment mitigation strategies to provide a unifying view of decoding as a tool for alignment. |
| Outcome: | The proposed taxonomy combines likelihood and utility assumptions to provide general statements about decoding as a tool for alignment across tasks. |
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)
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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen
| Challenge: | GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages. |
| Approach: | They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement. |
| Outcome: | The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3. |
Visual Enhanced Entity-Level Interaction Network for Multimodal Summarization (2024.findings-naacl)
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| Challenge: | Existing methods to generate concise summarizations rely on coarse-grained textual and visual information, but they are underutilized. |
| Approach: | They propose a Visual Enhanced Entity-Level Interaction Network to address underutilization of multimodal inputs at a fine-grained level. |
| Outcome: | The proposed model outperforms existing models on two MMS datasets and proposes new metrics to measure factual consistency of entities in the output. |
Investigating the Emergent Audio Classification Ability of ASR Foundation Models (2024.naacl-long)
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| Challenge: | Text and vision foundation models can perform many tasks in a zero-shot setting . however, there has been little work on the zero-shoot abilities of ASR foundation models . |
| Approach: | They investigate the ability of ASR foundation models to perform zero-shot audio classification using text prompts and a decoding probability generator. |
| Outcome: | The proposed model outperforms state-of-the-art models on audio classification datasets without training them on extra data or adding any parameters. |
WER We Stand: Benchmarking Urdu ASR Models (2025.coling-main)
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| Challenge: | This paper analyzes the performance of three ASR models for low-resource languages like Urdu . low-rural languages like urdu have significant gaps in accuracy and reliability . |
| Approach: | They evaluate the performance of three ASR models: Whisper, MMS, and Seamless-M4T . they present the first conversational speech dataset for benchmarking Urdu ASR systems . |
| Outcome: | The proposed model families outperform Whisper, MMS, and Seamless-M4T on two types of speech datasets. |
Multilingual Models for ASR in Chibchan Languages (2024.naacl-long)
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| Challenge: | Existing algorithms for low resource-intensive languages are not available for these languages . a paper comparing the performance of different models and algorithms for these extremely low resource languages is presented. |
| Approach: | They propose to fine-tune four ASR algorithms to create monolingual models for Bribri and Cabécar . they then use the best performing algorithm to train joint and transfer learning models for both languages . |
| Outcome: | The proposed algorithms are effective in both Bribri and Cabécar, but especially in Bribri. |
Towards Robust Speech Representation Learning for Thousands of Languages (2024.emnlp-main)
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William Chen, Wangyou Zhang, Yifan Peng, Xinjian Li, Jinchuan Tian, Jiatong Shi, Xuankai Chang, Soumi Maiti, Karen Livescu, Shinji Watanabe
| Challenge: | XEUS is a cross-lingual encoder for universal speech that can be trained on 1 million hours of data across 4057 languages. |
| Approach: | They propose a Cross-lingual Encoder for Universal Speech that can be trained on 1 million hours of data across 4057 languages and a newly created corpus of 7400+ hours from 4057 . |
| Outcome: | The proposed model outperforms state-of-the-art models on several benchmarks and outperfies MMS 1B and w2v-BERT 2.0 v2 by 0.8% and 4.4% respectively. |
What is lost in Normalization? Exploring Pitfalls in Multilingual ASR Model Evaluations (2024.emnlp-main)
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| Challenge: | Existing text normalization routines that target Indic scripts are flawed when applied to multilingual automatic speech recognition models. |
| Approach: | They propose to develop text normalization routines that leverage native linguistic expertise to ensure more robust and accurate evaluations of multilingual automatic speech recognition models. |
| Outcome: | The proposed normalization routines can be leveraged to improve performance metrics for Indic languages. |
Improving Language and Modality Transfer in Translation by Character-level Modeling (2025.acl-long)
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| Challenge: | Current translation systems cover only 5% of the world's languages . expanding to the long-tail of low-resource languages requires data-efficient methods that rely on cross-lingual and cross-modal knowledge transfer. |
| Approach: | They propose a character-based approach to improve adaptability to new languages and modalities by using a teacher-student approach and parallel translation data to obtain a SONAR character-level encoder. |
| Outcome: | The proposed model outperforms subword-based models in speech-to-text translation on the FLEURS benchmark on 33 languages and achieves state-of-the-art generalizability to unseen languages. |
D2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization (2023.findings-emnlp)
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| Challenge: | Existing studies focus on improving MMS models by filtering summary-unrelated visual features with implicit learning or explicitly complex training objectives. |
| Approach: | They propose a multimodal multimodal summarization task that aims to generate summaries in any language with document inputs in any languages and the corresponding image sequence. |
| Outcome: | The proposed task can generate summaries in any language with document inputs in any languages and the corresponding image sequence. |
Multilingual Turn-taking Prediction Using Voice Activity Projection (2024.lrec-main)
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| Challenge: | a monolingual model does not make good predictions when applied to other languages, but a multilingual model is able to discern the language of the input signal. |
| Approach: | They propose to use a multilingual voice activity projection model to predict voice activities of spoken dialogue participants in English, Mandarin, and Japanese data. |
| Outcome: | The proposed model predicts the upcoming voice activities of participants in dyadic dialogue on multilingual data, encompassing English, Mandarin, and Japanese. |