Rethinking Word-Level Auto-Completion in Computer-Aided Translation
- URL: http://arxiv.org/abs/2310.14523v2
- Date: Tue, 24 Oct 2023 06:48:07 GMT
- Title: Rethinking Word-Level Auto-Completion in Computer-Aided Translation
- Authors: Xingyu Chen and Lemao Liu and Guoping Huang and Zhirui Zhang and
Mingming Yang and Shuming Shi and Rui Wang
- Abstract summary: Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted Translation.
It aims at providing word-level auto-completion suggestions for human translators.
We introduce a measurable criterion to answer this question and discover that existing WLAC models often fail to meet this criterion.
We propose an effective approach to enhance WLAC performance by promoting adherence to the criterion.
- Score: 76.34184928621477
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted
Translation. It aims at providing word-level auto-completion suggestions for
human translators. While previous studies have primarily focused on designing
complex model architectures, this paper takes a different perspective by
rethinking the fundamental question: what kind of words are good
auto-completions? We introduce a measurable criterion to answer this question
and discover that existing WLAC models often fail to meet this criterion.
Building upon this observation, we propose an effective approach to enhance
WLAC performance by promoting adherence to the criterion. Notably, the proposed
approach is general and can be applied to various encoder-based architectures.
Through extensive experiments, we demonstrate that our approach outperforms the
top-performing system submitted to the WLAC shared tasks in WMT2022, while
utilizing significantly smaller model sizes.
Related papers
- Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement [102.22911097049953]
SIMA is a framework that enhances visual and language modality alignment through self-improvement.
It employs an in-context self-critic mechanism to select response pairs for preference tuning.
We demonstrate that SIMA achieves superior modality alignment, outperforming previous approaches.
arXiv Detail & Related papers (2024-05-24T23:09:27Z) - A Survey on Efficient Inference for Large Language Models [25.572035747669275]
Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks.
The substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios.
This paper presents a comprehensive survey of the existing literature on efficient LLM inference.
arXiv Detail & Related papers (2024-04-22T15:53:08Z) - A Large-Scale Evaluation of Speech Foundation Models [110.95827399522204]
We establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the foundation model paradigm for speech.
We propose a unified multi-tasking framework to address speech processing tasks in SUPERB using a frozen foundation model followed by task-specialized, lightweight prediction heads.
arXiv Detail & Related papers (2024-04-15T00:03:16Z) - SurreyAI 2023 Submission for the Quality Estimation Shared Task [17.122657128702276]
This paper describes the approach adopted by the SurreyAI team for addressing the Sentence-Level Direct Assessment task in WMT23.
The proposed approach builds upon the TransQuest framework, exploring various autoencoder pre-trained language models.
The evaluation utilizes Spearman and Pearson correlation coefficients, assessing the relationship between machine-predicted quality scores and human judgments.
arXiv Detail & Related papers (2023-12-01T12:01:04Z) - Coherent Entity Disambiguation via Modeling Topic and Categorical
Dependency [87.16283281290053]
Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities.
We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions.
We achieve new state-of-the-art results on popular ED benchmarks, with an average improvement of 1.3 F1 points.
arXiv Detail & Related papers (2023-11-06T16:40:13Z) - Learning Action-Effect Dynamics for Hypothetical Vision-Language
Reasoning Task [50.72283841720014]
We propose a novel learning strategy that can improve reasoning about the effects of actions.
We demonstrate the effectiveness of our proposed approach and discuss its advantages over previous baselines in terms of performance, data efficiency, and generalization capability.
arXiv Detail & Related papers (2022-12-07T05:41:58Z) - Word Sense Induction with Hierarchical Clustering and Mutual Information
Maximization [14.997937028599255]
Word sense induction is a difficult problem in natural language processing.
We propose a novel unsupervised method based on hierarchical clustering and invariant information clustering.
We empirically demonstrate that, in certain cases, our approach outperforms prior WSI state-of-the-art methods.
arXiv Detail & Related papers (2022-10-11T13:04:06Z) - Automated Speech Scoring System Under The Lens: Evaluating and
interpreting the linguistic cues for language proficiency [26.70127591966917]
We utilize classical machine learning models to formulate a speech scoring task as both a classification and a regression problem.
First, we extract linguist features under five categories (fluency, pronunciation, content, grammar and vocabulary, and acoustic) and train models to grade responses.
In comparison, we find that the regression-based models perform equivalent to or better than the classification approach.
arXiv Detail & Related papers (2021-11-30T06:28:58Z) - Enhancing Dialogue Generation via Multi-Level Contrastive Learning [57.005432249952406]
We propose a multi-level contrastive learning paradigm to model the fine-grained quality of the responses with respect to the query.
A Rank-aware (RC) network is designed to construct the multi-level contrastive optimization objectives.
We build a Knowledge Inference (KI) component to capture the keyword knowledge from the reference during training and exploit such information to encourage the generation of informative words.
arXiv Detail & Related papers (2020-09-19T02:41:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.