Due to the lack of parallel data, the mainstream fine-tuning-based domain adaptation methods have the overfitting problem in the translation of low-resource domains, and it is difficult for the model to learn the in-domain generalization knowledge. To address the above issue, in this work, we propose a novel Reinforcement Learning Domain Adaptation method for Neural Machine Translation (RLDA-NMT) in the low-resource domain. RLDA-NMT utilizes in-domain source monolingual data to make up for the lack of parallel data, and reinforces domain features learning to make the translation model learn the domain-specific knowledge more fully. Specifically, we first train a ranking-based model with a small-scale in-domain parallel corpus, and then adopt it as the reward model to select higher-quality generated translations for reinforcement when fine-tuning pre-trained NMT model using in-domain source monolingual data. We conduct experiments on Education, Laws, Thesis, and Patent domains of Chinese⇔English translation tasks. Experimental results demonstrate that RLDA-NMT can alleviate overfitting and reinforce the NMT model to learn domain-specific knowledge. Additionally, the results also show that RLDA-NMT and back-translation (BT) are nicely complementary to each other, where combining RLDA-NMT with BT can further improve translation quality.

Sentence-level translation, document-level translation, translation memory, and terminology constrained translation play an important role in machine translation.Most of the previous work uses separate models or methods to solve these tasks, which is not conducive to knowledge transfer of different tasks and increases the complexity of system construction.In this work, we explore the potential of pre-trained language model in machine translation tasks and propose a Multi-Task Machine Translation (MT2) model to integrate these translation tasks.We design a novel translationspecific In-Context Learning (ICL) paradigm for model training, in which all of the translation tasks can be modeled as context-learning tasks that integrate contextual information for performance improvement.Specifically, we propose a retrieval and alignment method to obtain a large scale context-enhancement training data, then we train the model in an in-context learning manner.Furthermore, we adopt two context-dependent training strategies to encourage the model to better understand and utilize contextual information for translation.Extensive experiments on translation memory, terminology constrained translation, document-level translation, and few-shot domain-adaptation tasks demonstrate the superior performance of our model, verifying the effectiveness of our proposed approach.

Negative samples have not been efficiently explored in multilingual dense passage retrieval.In this paper, we propose a novel multilingual dense passage retrieval framework, mHFN, to recover and utilize hard and false negative samples.mHFN consists of three key components: 1) a multilingual hard negative sample augmentation module that allows knowledge of indistinguishable passages to be shared across multiple languages and synthesizes new hard negative samples by interpolating representations of queries and existing hard negative samples, 2) a multilingual negative sample cache queue that stores negative samples from previous batches in each language to increase the number of multilingual negative samples used in training beyond the batch size limit, and 3) a lightweight adaptive false negative sample filter that uses generated pseudo labels to separate unlabeled false negative samples and converts them into positive passages in training.We evaluate mHFN on Mr. TyDi, a high-quality multilingual dense passage retrieval dataset covering eleven typologically diverse languages, and experimental results show that mHFN outperforms strong sparse, dense and hybrid baselines and achieves new stateof-the-art performance on all languages.Our source code is available at https://github. com/Magnetic2014/mHFN.

Generating coherent long texts is an important yet challenging task, particularly for the openended generation task.Prior work based on discrete latent codes focuses on the modeling of discourse relation, resulting in discrete codes only learning shallow semantics (Ji and Huang, 2021).A natural text always revolves around several related topics and the transition across them is natural and smooth.In this work, we investigate whether discrete latent codes can learn information of topics.To this end, we build a topic-aware latent code-guided text generation model.To encourage discrete codes to model information about topics, we propose a span-level bag-of-words training objective for the model.Automatic and manual evaluation experiments show that our method can generate more topic-relevant and coherent texts.

Previous works on syntactically controlled paraphrase generation heavily rely on largescale parallel paraphrase data that are not easily available for many languages and domains.In this paper, we take this research direction to the extreme and investigate whether it is possible to learn syntactically controlled paraphrase generation with non-parallel data.We propose a syntactically-informed unsupervised paraphrasing model based on conditional variational auto-encoder (VAE) which can generate texts in a specified syntactic structure.Particularly, we design a two-stage learning method to effectively train the model using non-parallel data.The conditional VAE is trained to reconstruct the input sentence according to the given input and its syntactic structure.Furthermore, to improve the syntactic controllability and semantic consistency of the pre-trained conditional VAE, we finetune it using syntax controlling and cycle reconstruction learning objectives, and employ Gumbel-Softmax to combine these new learning objectives.Experiment results demonstrate that the proposed model trained only on non-parallel data is capable of generating diverse paraphrases with specified structures.Additionally, we further validate the effectiveness of our method for generating syntactically adversarial examples on a sentiment analysis task.

Paraphrase generation (PG) is of great importance to many downstream tasks in natural language processing.Diversity is an essential nature to PG for enhancing generalization capability and robustness of downstream applications.Recently, neural sequence-to-sequence (Seq2Seq) models have shown promising results in PG.However, traditional model training for PG focuses on optimizing model prediction against single reference and employs cross-entropy loss, which objective is unable to encourage model to generate diverse paraphrases.In this work, we present a novel approach with multi-objective learning to PG.We propose a learning-exploring method to generate sentences as learning objectives from the learned data distribution, and employ reinforcement learning to combine these new learning objectives for model training.We first design a sample-based algorithm to explore diverse sentences.Then we introduce several reward functions to evaluate the sampled sentences as learning signals in terms of expressive diversity and semantic fidelity, aiming to generate diverse and high-quality paraphrases.To effectively optimize model performance satisfying different evaluating aspects, we use a GradNorm-based algorithm that automatically balances these training objectives.Experiments and analyses on Quora and Twitter datasets demonstrate that our proposed method not only gains a significant increase in diversity but also improves generation quality over several state-of-the-art baselines.

Paraphrase generation is of great importance to many downstream tasks in natural language processing. Recent efforts have focused on generating paraphrases in specific syntactic forms, which, generally, heavily relies on manually annotated paraphrase data that is not easily available for many languages and domains. In this paper, we propose a novel end-to-end framework to leverage existing large-scale bilingual parallel corpora to generate paraphrases under the control of syntactic exemplars. In order to train one model over the two languages of parallel corpora, we embed sentences of them into the same content and style spaces with shared content and style encoders using cross-lingual word embeddings. We propose an adversarial discriminator to disentangle the content and style space, and employ a latent variable to model the syntactic style of a given exemplar in order to guide the two decoders for generation. Additionally, we introduce cycle and masking learning schemes to efficiently train the model. Experiments and analyses demonstrate that the proposed model trained only on bilingual parallel data is capable of generating diverse paraphrases with desirable syntactic styles. Fine-tuning the trained model on a small paraphrase corpus makes it substantially outperform state-of-the-art paraphrase generation models trained on a larger paraphrase dataset.

Mingtong Liu, Yujie Zhang, Jinan Xu, Yufeng Chen. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.