Papers with ALT

2 papers
BERTSeg: BERT Based Unsupervised Subword Segmentation for Neural Machine Translation (2022.aacl-short)

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Challenge: Existing subword segmenters are frequency-based without semantics information or neural-based but trained on parallel corpora.
Approach: They propose an unsupervised neural subword segmenter for neural machine translation that utilizes contextualized semantic embeddings of words from characterBERT and maximizes the generation probability of subword segments.
Outcome: The proposed method improves translation performance on ALT, IWSLT15 Vi->En, WMT16 Ro->En and WMT15 Fi->En datasets.
Towards Aligning Language Models with Textual Feedback (2024.emnlp-main)

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Challenge: Using textual feedback, language models can be trained to learn from textual inputs.
Approach: They propose an approach that aligns language models with user preferences expressed in text.
Outcome: The proposed approach outperforms PPO on toxicity reduction, summarization, and dialog response tasks while achieving the same performance with only 20% of the samples.

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