Papers with modeling techniques
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents (2024.findings-eacl)
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| Challenge: | AccentFold uses spatial relationships to improve speech recognition for accented speech . existing methods for accent recognition have been limited due to data scarcity and budget constraints . |
| Approach: | They propose a method that exploits spatial relationships between learned accent embeddings to improve downstream automatic speech recognition. |
| Outcome: | The proposed method outperforms baseline methods in accented speech training. |
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers (2021.acl-long)
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| Challenge: | Recent large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQl parsing. |
| Approach: | They propose a new cross-domain evaluation dataset of real Web databases . they examine the choice of evaluation tasks for text-to-SQL parsers . |
| Outcome: | The proposed model improves accuracy by 13.2% over state-of-the-art parsers in real-life environments. |
Experience Grounds Language (2020.emnlp-main)
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Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian
| Challenge: | aaron carroll: language understanding research is held back by a failure to relate language to the physical world it describes and to social interactions it facilitates. carroll says successful linguistic communication relies on a shared experience of the world. |
| Approach: | They propose to use a broader physical and social context to address communication problems . they argue that the current success of representation learning approaches is limited . |
| Outcome: | a new study suggests that the current success of representation learning requires a parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication. |
Toward Machine Interpreting: Lessons from Human Interpreting Studies (2025.emnlp-main)
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| Challenge: | Current speech translation systems are static and do not adapt to real-world situations in ways human interpreters do. |
| Approach: | They propose to model human interpreting using a new language model to improve usability . they argue that there is great potential to adopt many human interpreted principles . |
| Outcome: | The proposed models can be used to improve human interpreting and improve translation performance. |