Papers with neural translation systems

3 papers
It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information (2020.acl-main)

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Challenge: Current state-of-the-art MT systems are based on neural networks, but it is unclear whether all translation directions are equally easy (or hard) to model for NMT.
Approach: They propose an asymmetric information-theoretic metric of machine translation difficulty that exploits the probabilistic nature of most neural machine translation models.
Outcome: The proposed metric allows us to better evaluate the difficulty of translating text into the target language while controlling for the difficulty independent of the translation task.
Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM’s Translation Capability (2023.acl-long)

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Challenge: Large multilingual language models exhibit impressive zero- or few-shot machine translation capabilities, despite never having been explicitly and intentionally exposed to translation data.
Approach: They propose a mixed-method approach to measure and understand incidental bilingualism at scale using the Pathways Language Model.
Outcome: The proposed model is exposed to over 30 million translation pairs across at least 44 languages.
How effective is machine translation on low-resource code-switching? A case study comparing human and automatic metrics (2023.findings-acl)

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Challenge: Specifically, we compare the performance of three MT systems in terms of their ability to translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively.
Approach: They compare the performance of three machine translation systems in the context of machine translation (MT) they find that state-of-the-art neural translation systems achieve higher scores on automatic metrics when processing CSW input .
Outcome: The proposed system can translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively.

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