Integrating Vectorized Lexical Constraints for Neural Machine Translation

• URL: http://arxiv.org/abs/2203.12210v1
• Date: Wed, 23 Mar 2022 05:54:37 GMT
• Title: Integrating Vectorized Lexical Constraints for Neural Machine Translation
• Authors: Shuo Wang, Zhixing Tan, Yang Liu
• Abstract summary: Lexically constrained neural machine translation (NMT) controls the generation of NMT models with pre-specified constraints. Most existing works choose to construct synthetic data or modify the decoding algorithm to impose lexical constraints, treating the NMT model as a black box. We propose to open this black box by directly integrating the constraints into NMT models.
• Score: 22.300632179228426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lexically constrained neural machine translation (NMT), which controls the generation of NMT models with pre-specified constraints, is important in many practical scenarios. Due to the representation gap between discrete constraints and continuous vectors in NMT models, most existing works choose to construct synthetic data or modify the decoding algorithm to impose lexical constraints, treating the NMT model as a black box. In this work, we propose to open this black box by directly integrating the constraints into NMT models. Specifically, we vectorize source and target constraints into continuous keys and values, which can be utilized by the attention modules of NMT models. The proposed integration method is based on the assumption that the correspondence between keys and values in attention modules is naturally suitable for modeling constraint pairs. Experimental results show that our method consistently outperforms several representative baselines on four language pairs, demonstrating the superiority of integrating vectorized lexical constraints.

Related papers

• Simultaneous Machine Translation with Large Language Models [51.470478122113356]
  We investigate the possibility of applying Large Language Models to SimulMT tasks. We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset. The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
  arXiv  Detail & Related papers  (2023-09-13T04:06:47Z)
• Disambiguated Lexically Constrained Neural Machine Translation [20.338107081523212]
  Current approaches to LCNMT assume that the pre-specified lexical constraints are contextually appropriate. We propose disambiguated LCNMT (D-LCNMT) to solve the problem. D-LCNMT is a robust and effective two-stage framework that disambiguates the constraints based on contexts at first, then integrates the disambiguated constraints into LCNMT.
  arXiv  Detail & Related papers  (2023-05-27T03:15:10Z)
• Symmetric Tensor Networks for Generative Modeling and Constrained Combinatorial Optimization [72.41480594026815]
  Constrained optimization problems abound in industry, from portfolio optimization to logistics. One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space. In this work, we encode arbitrary integer-valued equality constraints of the form Ax=b, directly into U(1) symmetric networks (TNs) and leverage their applicability as quantum-inspired generative models.
  arXiv  Detail & Related papers  (2022-11-16T18:59:54Z)
• A Template-based Method for Constrained Neural Machine Translation [100.02590022551718]
  We propose a template-based method that can yield results with high translation quality and match accuracy while keeping the decoding speed. The generation and derivation of the template can be learned through one sequence-to-sequence training framework. Experimental results show that the proposed template-based methods can outperform several representative baselines in lexically and structurally constrained translation tasks.
  arXiv  Detail & Related papers  (2022-05-23T12:24:34Z)
• Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation [1.7616042687330642]
  A key step to validate the robustness of the NMT models is to evaluate their performance on adversarial inputs. In this work, we identify a set of perturbations and metrics tailored for the robustness assessment of such models. We present a preliminary experimental evaluation, showing what type of perturbations affect the model the most.
  arXiv  Detail & Related papers  (2022-03-29T08:01:39Z)
• Rule-based Morphological Inflection Improves Neural Terminology Translation [16.802947102163497]
  We introduce a modular framework for incorporating lemma constraints in neural MT (NMT) It is based on a novel cross-lingual inflection module that inflects the target lemma constraints based on the source context. Results show that our rule-based inflection module helps NMT models incorporate lemma constraints more accurately than a neural module and outperforms the existing end-to-end approach with lower training costs.
  arXiv  Detail & Related papers  (2021-09-10T02:06:48Z)
• Exploring Unsupervised Pretraining Objectives for Machine Translation [99.5441395624651]
  Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT) Most approaches adapt masked-language modeling (MLM) to sequence-to-sequence architectures, by masking parts of the input and reconstructing them in the decoder. We compare masking with alternative objectives that produce inputs resembling real (full) sentences, by reordering and replacing words based on their context.
  arXiv  Detail & Related papers  (2021-06-10T10:18:23Z)
• Early Stage LM Integration Using Local and Global Log-Linear Combination [46.91755970827846]
  Sequence-to-sequence models with an implicit alignment mechanism (e.g. attention) are closing the performance gap towards traditional hybrid hidden Markov models (HMM) One important factor to improve word error rate in both cases is the use of an external language model (LM) trained on large text-only corpora. We present a novel method for language model integration into implicit-alignment based sequence-to-sequence models.
  arXiv  Detail & Related papers  (2020-05-20T13:49:55Z)
• DiscreTalk: Text-to-Speech as a Machine Translation Problem [52.33785857500754]
  This paper proposes a new end-to-end text-to-speech (E2E-TTS) model based on neural machine translation (NMT) The proposed model consists of two components; a non-autoregressive vector quantized variational autoencoder (VQ-VAE) model and an autoregressive Transformer-NMT model.
  arXiv  Detail & Related papers  (2020-05-12T02:45:09Z)
• Lexically Constrained Neural Machine Translation with Levenshtein Transformer [8.831954614241234]
  This paper proposes a simple and effective algorithm for incorporating lexical constraints in neural machine translation. Our method injects terminology constraints at inference time without any impact on decoding speed.
  arXiv  Detail & Related papers  (2020-04-27T09:59:27Z)
• Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck [52.08901549360262]
  Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. VAEs tend to ignore latent variables with a strong auto-regressive decoder. We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
  arXiv  Detail & Related papers  (2020-04-22T14:41:37Z)

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.