Currently, Large Language Models (LLMs) have achieved remarkable results in machine translation.However, their performance in multi-domain translation (MDT) is less satisfactory, the meanings of words can vary across different domains, highlighting the significant ambiguity inherent in MDT.Therefore, evaluating the disambiguation ability of LLMs in MDT remains an open problem.To this end, we present an evaluation and analysis of LLMs on disambiguation in multi-domain translation (DMDTEval), our systematic evaluation framework consisting of three aspects: (1) we construct a translation test set with multi-domain ambiguous word annotation, (2) we curate a diverse set of disambiguation prompt strategies, and (3) we design precise disambiguation metrics, and study the efficacy of various prompt strategies on multiple state-of-the-art LLMs.We conduct comprehensive experiments across 4 language pairs and 13 domains, our extensive experiments reveal a number of crucial findings that we believe will pave the way and also facilitate further research in the critical area of improving the disambiguation of LLMs.

Multi-domain neural machine translation aims to construct a unified neural machine translation model to translate sentences across various domains. Nevertheless, previous studies have one limitation is the incapacity to acquire both domain-general and domain-specific representations concurrently. To this end, we propose an ensemble strategy with gradient conflict for multi-domain neural machine translation that automatically learns model parameters by identifying both domain-shared and domain-specific features. Specifically, our approach consists of (1) a parameter-sharing framework, where the parameters of all the layers are originally shared and equivalent to each domain, and (2) ensemble strategy, in which we design an Extra Ensemble strategy via a piecewise condition function to learn direction and distance-based gradient conflict. In addition, we give a detailed theoretical analysis of the gradient conflict to further validate the effectiveness of our approach. Experimental results on two multi-domain datasets show the superior performance of our proposed model compared to previous work.

Due to the strong reliance on domain-specific knowledge, the joint learning manner of domain discrimination and translation has been widely considered in the Multi-Domain Neural Machine Translation (MDNMT) task. However, the word ambiguity problem still inevitably exists in MDNMT, especially when mixed multi-domain data is brought into the model training phase. Although word-level MDNMT can mitigate this problem to some extent, poor domain discrimination yet remains and severely hinders performance. Based on the above limitation, we observed that coarser granularity strings may provide more specific semantics, which is more conducive to domain discrimination. Thus, we propose a Word-level Domain-Sensitive Representation Learning (WDSRL) method. Specifically, we focus on two aspects of our approach: domain representation and domain discrimination. To extend the scope of domain representation, we adopt Convolution Neural Networks (CNN) to encode Local Domain Representation at different granularities, and then integrate Topic Knowledge Representation into each word. By doing so, context features related to the domain could be comprehensively enriched. Regarding domain discrimination, we design a Domain-Sensitive Discriminator, which could not only generate domain features for each word but also enhance domain representation learning. Experimental results demonstrate our substantial improvements over several representative baselines on multiple language pairs. Furthermore, the extensive analysis also indicates the superiority of our proposed domain-sensitive feature encoding strategy and domain-sensitive discriminator for word-level representation learning.

Currently, multi-domain neural machine translation (NMT) has become a significant research topic in domain adaptation machine translation, which trains a single model by mixing data from multiple domains. Multi-domain NMT aims to improve the performance of the low-resources domain through data augmentation. However, mixed domain data brings more translation ambiguity. Previous work focused on domain-general or domain-context knowledge learning, respectively. Therefore, there is a challenge for acquiring domain-general or domain-context knowledge simultaneously. To this end, we propose a unified framework for learning simultaneously domain-general and domain-specific knowledge, we are the first to apply parameter differentiation in multi-domain NMT. Specifically, we design the differentiation criterion and differentiation granularity to obtain domain-specific parameters. Experimental results on multi-domain UM-corpus English-to-Chinese and OPUS German-to-English datasets show that the average BLEU scores of the proposed method exceed the strong baseline by 1.22 and 1.87, respectively. In addition, we investigate the case study to illustrate the effectiveness of the proposed method in acquiring domain knowledge.

Motivated from the convergence capability achieved by gradient neural network (GNN) and zeroing neural network (ZNN) for matrix inversion, in this article, a novel hybrid GNN-ZNN (H-GNN-ZNN) model is proposed by introducing a fuzzy adaptive control strategy to generate a fuzzy adaptive factor that can change its size adaptively according to the residual error. Due to its fuzzy adaptability, this novel model is called the fuzzy adaptive GNN-ZNN (FA-GNN-ZNN) model for presentation convenience. We prove that the FA-GNN-ZNN model has the better performance than the existing H-GNN-ZNN model under the same conditions. In addition, different activation functions are applied to the FA-GNN-ZNN model to improve its performance further, and the corresponding theoretical analysis is given. Finally, comparative simulation results demonstrate the validity and superiority of the FA-GNN-ZNN model for matrix inversion.