Neural Speech Translation using Lattice Transformations and Graph Networks (D19-53)
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| Challenge: | Existing work on end-to-end systems bypass the need for intermediate representations, but this approach is limited in practical applications. |
| Approach: | They propose a lattice-tosequence model which uses lattics as encoders and graph networks to address two problems by applying latticae transformations and a neural model. |
| Outcome: | The proposed model beats pipeline approaches while being orders of magnitude faster than previous work. |
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| Challenge: | Recent advances in sequence modeling have highlighted the strengths of the transformer architecture. |
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| Challenge: | Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. |
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| Challenge: | Existing methods for pretraining language models do not consider spoken language properties. |
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Simple and Effective Unsupervised Speech Translation (2023.acl-long)
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Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
| Challenge: | Existing methods to train speech models without labeled data are limited for most languages. |
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Pre-trained language model representations for language generation (N19-1)
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| Challenge: | Pre-trained language model representations have been successful in a wide range of language understanding tasks. |
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Thesis Proposal: Development of End-to-End Speech Translation Models for Indian Languages (2026.eacl-srw)
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| Challenge: | Existing approaches to speech-to-speech translation rely on cascaded pipelines . current approaches rely only on text representations, but they suffer from errors and latency . a new direct speech translation framework is proposed to bridge linguistic gaps . |
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Speech Translation and the End-to-End Promise: Taking Stock of Where We Are (2020.acl-main)
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| Challenge: | Until recently, the only feasible approach to translating acoustic speech signals into text was the cascaded approach. |
| Approach: | They propose a classification of the main challenges of traditional approaches to speech translation . they argue that end-to-end models fall short due to compromises made to address data scarcity . |
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Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging (2022.emnlp-main)
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| Challenge: | Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-resource languages lack labeled training data. |
| Approach: | They propose a method for transferring labels from high-resource sources to low-resourced target languages using a graph-based label propagation method. |
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