Challenge: Translation difficulty is a problem when translators are required to resolve translation ambiguity from multiple possible translations.
Approach: They use word alignments computed over large scale bilingual corpora to develop predictors of lexical translation difficulty.
Outcome: The proposed method improves on a previous embedding-based approach and can contribute to a deeper understanding of cross-lingual differences and of causes of translation difficulty.

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Challenge: Accurate estimation of item (question or task) difficulty suffers from the cold start problem.
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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.
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)

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Challenge: Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models.
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What Kind of Language Is Hard to Language-Model? (P19-1)

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Challenge: a recent study suggests that language models perform poorly across languages.
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Aligning Translation-Specific Understanding to General Understanding in Large Language Models (2024.emnlp-main)

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Challenge: Large Language models (LLMs) have remarkable abilities in understanding complex texts . however, understanding misalignment leads to LLMs mistakenly translating complex concepts .
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Challenge: a recent study revisits six core challenges that have influenced the evolution of Neural Machine Translation (NMT) domain mismatch, amount of parallel data, rare word prediction, translation of long sentences and sub-optimal beam search remain challenges in LLMs.
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Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation (N18-2)

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Challenge: Using word similarities, we improve word alignments in low resource settings . word alignment is essential for statistical machine translation (MT)
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Are we Estimating or Guesstimating Translation Quality? (2020.acl-main)

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Challenge: A carefully engineered ensemble of pre-trained multilingual language models won the QE shared task at WMT19.
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Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment (2022.emnlp-main)

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Challenge: Existing word alignment models capture few interactions between input sentence pairs, which severely degrades the word alignment quality.
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Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems (2025.findings-emnlp)

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Challenge: Currently, traditional evaluation methods struggle to detect subtle translation errors.
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