The Unreasonable Volatility of Neural Machine Translation Models

• URL: http://arxiv.org/abs/2005.12398v1
• Date: Mon, 25 May 2020 20:54:23 GMT
• Title: The Unreasonable Volatility of Neural Machine Translation Models
• Authors: Marzieh Fadaee and Christof Monz
• Abstract summary: We investigate the unexpected volatility of NMT models where the input is semantically and syntactically correct. We find that both RNN and Transformer models display volatile behavior in 26% and 19% of sentence variations, respectively.
• Score: 5.44772285850031
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent works have shown that Neural Machine Translation (NMT) models achieve impressive performance, however, questions about understanding the behavior of these models remain unanswered. We investigate the unexpected volatility of NMT models where the input is semantically and syntactically correct. We discover that with trivial modifications of source sentences, we can identify cases where \textit{unexpected changes} happen in the translation and in the worst case lead to mistranslations. This volatile behavior of translating extremely similar sentences in surprisingly different ways highlights the underlying generalization problem of current NMT models. We find that both RNN and Transformer models display volatile behavior in 26% and 19% of sentence variations, respectively.

Related papers

• Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective [72.83966378613238]
  Under-translation and over-translation remain two challenging problems in state-of-the-art Neural Machine Translation (NMT) systems. We conduct an in-depth analysis on the underlying cause of under-translation in NMT, providing an explanation from the perspective of decoding objective. We propose employing the confidence of predicting End Of Sentence (EOS) as a detector for under-translation, and strengthening the confidence-based penalty to penalize candidates with a high risk of under-translation.
  arXiv  Detail & Related papers  (2024-05-29T09:25:49Z)
• Context-Aware Machine Translation with Source Coreference Explanation [26.336947440529713]
  We propose a model that explains the decisions made for translation by predicting coreference features in the input. We evaluate our method in the WMT document-level translation task of English-German dataset, the English-Russian dataset, and the multilingual TED talk dataset.
  arXiv  Detail & Related papers  (2024-04-30T12:41:00Z)
• Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning [24.64700139151659]
  Current Neural machine translation (NMT) systems suffer from a lack of reliability. We present a consistency-aware meta-learning (CAML) framework derived from the model-agnostic meta-learning (MAML) algorithm to address it. We conduct experiments on the NIST Chinese to English task, three WMT translation tasks, and the TED M2O task.
  arXiv  Detail & Related papers  (2023-03-20T09:41:28Z)
• 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)
• When Does Translation Require Context? A Data-driven, Multilingual Exploration [71.43817945875433]
  proper handling of discourse significantly contributes to the quality of machine translation (MT) Recent works in context-aware MT attempt to target a small set of discourse phenomena during evaluation. We develop the Multilingual Discourse-Aware benchmark, a series of taggers that identify and evaluate model performance on discourse phenomena.
  arXiv  Detail & Related papers  (2021-09-15T17:29:30Z)
• Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation [14.645468999921961]
  We analyze the impact of different types of fine-grained semantic divergences on Transformer models. We introduce a divergent-aware NMT framework that uses factors to help NMT recover from the degradation caused by naturally occurring divergences.
  arXiv  Detail & Related papers  (2021-05-31T16:15:35Z)
• Recoding latent sentence representations -- Dynamic gradient-based activation modification in RNNs [0.0]
  In RNNs, encoding information in a suboptimal way can impact the quality of representations based on later elements in the sequence. I propose an augmentation to standard RNNs in form of a gradient-based correction mechanism. I conduct different experiments in the context of language modeling, where the impact of using such a mechanism is examined in detail.
  arXiv  Detail & Related papers  (2021-01-03T17:54:17Z)
• On Long-Tailed Phenomena in Neural Machine Translation [50.65273145888896]
  State-of-the-art Neural Machine Translation (NMT) models struggle with generating low-frequency tokens. We propose a new loss function, the Anti-Focal loss, to better adapt model training to the structural dependencies of conditional text generation. We show the efficacy of the proposed technique on a number of Machine Translation (MT) datasets, demonstrating that it leads to significant gains over cross-entropy.
  arXiv  Detail & Related papers  (2020-10-10T07:00:57Z)
• On the Inference Calibration of Neural Machine Translation [54.48932804996506]
  We study the correlation between calibration and translation performance and linguistic properties of miscalibration. We propose a new graduated label smoothing method that can improve both inference calibration and translation performance.
  arXiv  Detail & Related papers  (2020-05-03T02:03:56Z)
• Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models [72.56058378313963]
  We bridge the gap by assessing the bilingual knowledge learned by NMT models with phrase table. We find that NMT models learn patterns from simple to complex and distill essential bilingual knowledge from the training examples.
  arXiv  Detail & Related papers  (2020-04-28T03:44:34Z)
• Explicit Reordering for Neural Machine Translation [50.70683739103066]
  In Transformer-based neural machine translation (NMT), the positional encoding mechanism helps the self-attention networks to learn the source representation with order dependency. We propose a novel reordering method to explicitly model this reordering information for the Transformer-based NMT. The empirical results on the WMT14 English-to-German, WAT ASPEC Japanese-to-English, and WMT17 Chinese-to-English translation tasks show the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2020-04-08T05:28:46Z)

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.