Papers with READ
Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model (D19-59)
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| Challenge: | 96,557 words were rated using the ‘Word List by Semantic Principles’ . 96 participants were surveyed using Yahoo! crowdsourcing . |
| Approach: | They used Bayesian linear mixed models to estimate word familiarity rates using the ‘Word List by Semantic Principles’ and the semantic labels used in the study. |
| Outcome: | The proposed method estimated word familiarity rates using Bayesian linear mixed models and semantic labels. |
Robust Natural Language Understanding with Residual Attention Debiasing (2023.findings-acl)
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| Challenge: | Existing ensemble-based debiasing methods do not address unintended dataset biases . attention plays a crucial role in providing robust prediction in NLU models . |
| Approach: | They propose an end-to-end debiasing method that mitigates unintended biases from attention. |
| Outcome: | The proposed method improves the OOD performance of BERT-based models on three benchmarks. |
Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation (D19-1)
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| Challenge: | Recent work on simultaneous translation is difficult because of its latency and quality. |
| Approach: | They propose a supervised-learning framework to learn adaptive policies from parallel text sequences . they use a model that predicts when a target word is read or WRITE if context provides enough information . |
| Outcome: | Experiments on German=>English show that the proposed method can learn flexible policies with better BLEU scores and similar latencies compared to previous work. |
Modeling Dual Read/Write Paths for Simultaneous Machine Translation (2022.acl-long)
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| Challenge: | Simultaneous machine translation (SiMT) outputs translation while reading source sentence . existing methods do not direct the read/write path, resulting in poor performance . |
| Approach: | They propose a method which introduces duality constraints to direct the read/write path . they propose to map the read path in two SiMT models to satisfy duality constraint . |
| Outcome: | Experiments on En-Vi and De-En tasks show that the proposed method outperforms baselines under all latency. |
Non-autoregressive Streaming Transformer for Simultaneous Translation (2023.emnlp-main)
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| Challenge: | Simultaneous machine translation models are trained to strike a balance between latency and translation quality. |
| Approach: | They propose a non-autoregressive streaming Transformer which generates blank tokens and decodes repetitive tokens to adjust its READ/WRITE strategy flexibly. |
| Outcome: | The proposed model outperforms previous strong autoregressive models on various benchmarks on siMT. |
Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies (2026.findings-acl)
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| Challenge: | Simultaneous machine translation requires high-quality translations under strict real-time constraints. |
| Approach: | They extend the action space of simultaneous machine translation with four adaptive actions . they adapt these actions in a large language model framework and construct training references . |
| Outcome: | The proposed framework improves semantic metrics and achieves lower delay compared to reference translations and salami-based baselines. |
Enhanced Simultaneous Machine Translation with Word-level Policies (2023.findings-emnlp)
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| Challenge: | Existing studies assume that operations are carried out at the subword level . a novel policy dictates whether to READ or WRITE at each step of the translation process . |
| Approach: | They propose a method to boost SiMT models using language models to address subword disparity . they propose implementing a word-level policy that dictates whether to READ or WRITE . |
| Outcome: | The proposed policy improves the performance of SiMT models by boosting them with language models . the proposed policy plays a vital role in addressing the subword disparity between LMs and SiMT systems. |