Accurate Online Posterior Alignments for Principled Lexically-Constrained Decoding (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods of offline alignment use only the entire target sentence. |
| Approach: | They propose a posterior alignment technique that is truly online in its execution and superior in terms of alignment error rates compared to existing methods. |
| Outcome: | The proposed technique is online in execution and superior in alignment error rates compared to existing methods. |
Similar Papers
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation (N18-1)
Copied to clipboard
| Challenge: | Existing approaches to neural machine translation have computational complexities that are either linear or exponential in the number of constraints. |
| Approach: | They propose an algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. |
| Outcome: | The proposed algorithm can place constraints and improve results in simulated post-editing tasks. |
Constraining word alignments with posterior regularization for label transfer (2022.naacl-industry)
Copied to clipboard
| Challenge: | Unsupervised word alignments are not always possible in industrial NLP pipelines, where multilingual annotation guidelines are complex and deviate from semantic consistency due to various factors. |
| Approach: | They propose to constrain word alignment models to remain consistent with both source and target annotation guidelines by leveraging posterior regularization and labeled examples. |
| Outcome: | The proposed model improves on the multiATIS++ dataset over AWESoME, and even a small amount of target language annotations can help. |
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting (N19-1)
Copied to clipboard
J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
| Challenge: | Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in machine translation or monolingual text rewriting tasks. |
| Approach: | They propose a vectorized dynamic beam allocation algorithm which extends work in lexically-constrained decoding to work with batching. |
| Outcome: | The proposed method improves on natural language inference, question answering and machine translation tasks by fivefold . |
Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
Copied to clipboard
| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
| Outcome: | The proposed methods can be applied to encoder models and encoder-decoder-only models . they show that language-neutral and language-specific information is key . |
End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to enforce word forms in translations struggle to make them agree with the rest of the output. |
| Approach: | They propose to train neural machine translation models with lemmatized constraints to infer correct word inflection. |
| Outcome: | The proposed model reduces errors in translation of constrained terms in automatic and manual evaluations on English-Czech language pairs. |
Alignment-Augmented Speculative Decoding with Alignment Sampling and Conditional Verification (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to accelerate autoregressive generation of large language models require training costs. |
| Approach: | They propose a training-free alignment-augmented speculative decoding algorithm . it leverages the output distribution obtained in the prefilling phase to provide more aligned draft candidates . |
| Outcome: | The proposed method increases the average generation score by 3.3 points for the LLaMA3 model. |
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing work on cross-lingual semantic parsing has focused on English . a few-shot approach to parse from natural languages is comparatively unexplored . |
| Approach: | They propose a method that minimizes cross-lingual divergence between probabilistic latent variables by Optimal Transport. |
| Outcome: | The proposed method improves performance even without parallel input translations on two datasets. |
Nudging: Inference-time Alignment of LLMs via Guided Decoding (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) require alignment to effectively and safely follow user instructions. |
| Approach: | They propose a simple, training-free algorithm that aligns any base model at inference time using a small aligned model. |
| Outcome: | The proposed algorithm outperforms large aligned models on open-instruction tasks without training. |
Neural Machine Translation Decoding with Terminology Constraints (N18-2)
Copied to clipboard
| Challenge: | Constrained neural machine translation systems can provide excellent quality but do not strictly enforce terminology. |
| Approach: | They propose a framework for constrained neural decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. |
| Outcome: | The proposed framework performs well on multiple translation tasks and motivates the need for constrained decoding with attentions to reduce misplacement and duplication when translating user constraints. |
Lexically Constrained Neural Machine Translation with Levenshtein Transformer (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to incorporate lexical constraints in neural machine translation have been unsuccessful . |
| Approach: | They propose an algorithm that incorporates lexical constraints into neural machine translation. |
| Outcome: | The proposed method improves on English-German datasets without modification . it does not require any modification to the training procedure and can be easily applied at runtime with custom dictionaries. |