The performance of neural machine translation relies on a large amount of data, but crawled sentence pairs are of different quality. The low-quality sentence pairs may provide helpful translation knowledge but also teach the model to generate low-quality translations. Making the model aware of the quality of training instances may help the model distinguish between good and bad translations while leveraging the translation knowledge. In this paper, we evaluate the quality of training instances with the average per-token loss (negative log-likelihood) from translation models, convert the quality scores into embeddings through vector interpolation and feed the quality embedding into the translation model during its training. We ask the model to decode with the best quality score to generate good translations during inference. Experiments on the IWSLT 14 German to English, WMT 14 English to German and WMT 22 English to Japanese translation tasks show that our method can effectively lead to consistent and significant improvements across multiple metrics.
This paper addresses the challenges of data scarcity and limited speaker resources in Lao-English code-switched speech synthesis. We propose a neural encoder-decoder-based method for mixed-lingual speech synthesis. The method first extracts phoneme-level speech representations and employs a dot-product attention mechanism to map Lao and English phonemes into a shared latent space, thereby enhancing the model's capability to represent cross-lingual phonetic information. In addition, language ID embedding module is extended to explicitly indicate the language of each input token, helping the model distinguish and adapt to language-specific pronunciation characteristics. Experiments are conducted on the open-source English dataset LibriTTS and a proprietary Lao speech corpus. Both subjective evaluations (MOS, AB preference tests) and objective metrics (RMSE) demonstrate that the proposed approach significantly outperforms the baseline VALL-E X model in terms of naturalness and language-switching fluency. Furthermore, ablation studies confirm that both the shared phoneme latent space and the language ID module play critical roles in improving synthesis quality. This approach offers a novel solution for integrating lowresource languages into mixed-lingual speech synthesis.
Large Language Models (LLMs) inevitably suffer from hallucinations, as relying solely on their parametric knowledge cannot guarantee the accuracy of generated content. To enhance text generation, retrieval-augmented generation (RAG) is proposed to incorporate external knowledge to achieve this. However, its effectiveness heavily depends on the relevance of retrieved documents, which poses a critical challenge: how to ensure the accuracy and reliability of model responses when retrieval results are inaccurate. Tackling this challenge, we propose Retrieval Judgment Augmented Generation (RJAG), a method that can enhance RAG through LLM-driven fine-grained relevance judgment mechanism and a task-adaptive knowledge combination strategy. RJAG judges and dynamically combines retrieved documents for both open-ended generation and closed-ended selection tasks. Additionally, largescale web search is also included to expand the knowledge beyond static corpora. Experimental results on multiple benchmarks show that RJAG outperforms existing RAG methods, which will significantly enhance the accuracy and reliability while maintaining the system's simplicity. Code is available at https://github.com/wangkz2023/RJAG.
It is widely known that the first language (L1) of the English learners will influence their language study, causing them make to biased errors. However, it is relatively limited for the research of using the L1 information to improve Grammatical Error Correction (GEC) models. Among the limited research, a common method is to train a set of GEC models, and each model is trained by a corpus from one (and only one) specific L1 background. This method has been proven efficient, while the waste of the training/fine-tuning data makes it suffer from the data limitation issue. This paper introduces a novel method to address this issue by exploiting the linguistic similarities between a language family and its member languages. We expand the fine-tuning data from one specific L1 background to its language family one, making the quantity increase exponentially. We use the Italic language family corpus as our language family corpus and experiment with two approaches facing two situations, mainly differing in development data. The results show that, for the approach that uses the Italic language family corpus to be the fine-tuning data and uses the development data where the L1 background is the same as the one of the test data, the GEC models improve clearly; however, the way that influences the models is not uniform, and varies by error types.
Addressing the limitations of the Skip-gram with Negative Sampling (SGNS) model related to negative sampling, subsampling, and its fixed context window mechanism, this paper first presents an in-depth statistical analysis of the optimal solution for SGNS matrix factorization, deriving the theoretically optimal distribution for negative sampling. Building upon this analysis, we propose the concept of Global Semantic Weight (GSW), derived from Pointwise Mutual Information (PMI). We integrate GSW with word frequency information to improve the effectiveness of both negative sampling and subsampling. Furthermore, we design dynamic adjustment mechanisms for the context window size and the number of negative samples based on GSW, enabling the model to adaptively capture contextual information commensurate with the semantic importance of the center word. Notably, our optimized model maintains the same time complexity as the original SGNS implementation. Experimental results demonstrate that our proposed model achieves competitive performance aganist state-of-the-art word embedding models including SGNS, CBOW, and GloVe, across multiple benchmark tasks. Compared with the current mainstream dynamic word vector models, this work emphasizes achieving a balance between efficiency and performance within a static embedding framework, and provides potential supplementation and support for complex models such as LLMs.
