Discourse-level Features for Statistical Machine Translation

Machine Translation (MT) has progressed tremendously in the past two decades. The rule-based and interlingua approaches have been superseded by statistical models, which learn the most likely translations from large parallel corpora. System design does not amount anymore to crafting syntactical transfer rules, nor does it rely on a semantic representation of the text. Instead, a statistical MT system learns the most likely correspondences and re-ordering of chunks of source words and target words from parallel corpora that have been word-aligned. With this procedure and millions of parallel source and target language sentences, systems can generate translations that are intelligible and require minimal post-editing efforts from the human user. Nevertheless, it has been recognized that the statistical MT paradigm may fall short of modeling a number of linguistic phenomena that are established beyond the phrase level. Research in statistical MT has addressed discourse phenomena explicitly only in the past four years. When it comes to textual coherence structure, cohesive ties relate sentences and entire paragraphs argumentatively to each other. This text structure has to be rendered appropriately in the target text so that it conveys the same meaning as the source text. The lexical and syntactical means through which these cohesive markers are expressed may diverge considerably between languages. Frequently, these markers include discourse connectives, which are function words such as however, instead, since, while, which relate spans of text to each other, e.g. for temporal ordering, contrast or causality. Moreover, to establish the same temporal ordering of events described in a text, the conjugation of verbs has to be coherently translated. The present thesis proposes methods for integrating discourse features into statistical MT. We pre-process the source text prior to automatic translation, focusing on two specific discourse phenomena: discourse connectives and verb tenses. Hand-crafted rules are not required in our proposal; instead, machine learning classifiers are implemented that learn to recognize discourse relations and predict translations of verb tenses. Firstly, we have designed new sets of semantically-oriented features and classifiers to advance the state of the art in automatic disambiguation of discourse connectives. We hereby profited from our multilingual setting and incorporated features that are based on MT and on the insights we gained from contrastive linguistic analysis of parallel corpora. In their best configurations, our classifiers reach high performances (0.7 to 1.0 F1 score) and can therefore reliably be used to automatically annotate the large corpora needed to train SMT systems. Issues of manual annotation and evaluation are discussed as well, and solutions are provided within new annotation and evaluation procedures. As a second contribution, we implemented entire SMT systems that can make use of the (automatically) annotated discourse information. Overall, the thesis confirms that these techniques are a practical solution that leads to global improvements in translation in ranges of 0.2 to 0.5 BLEU score. Further evaluation reveals that in terms of connectives and verb tenses, our statistical MT systems improve the translation of these phenomena in ranges of up to 25%, depending on the performance of the automatic classifiers and on the data sets used.

Discourse-level features for statistical machine translation

Thomas Meyer

EPFL2015

Discourse Phenomena in Machine Translation

Lesly Sadiht Miculicich Werlen

Machine Translation (MT) has made considerable progress in the past two decades, particularly after the introduction of neural network models (NMT). During this time, the research community has mostly focused on modeling and evaluating MT systems at the sentence level. MT models learn to translate from large amounts of parallel sentences in different languages. The focus on sentences brings a practical simplification for the task that favors efficiency but has the disadvantage of missing relevant contextual information. Several studies showed that the negative impact of this simplification is significant. One key point is that the discourse dependencies among distant words are ignored, resulting in a lack of coherence and cohesion in the text. The main objective of this thesis is to improve MT by including discourse-level constraints. In particular, we focus on the translation of the entity mentions. We summarize our contributions in four points. First, we define the evaluation process to assess entity translations (i.e., nouns and pronouns) and propose an automatic metric to measure this phenomenon. Second, we perform a proof-of-concept and analyze how effective it is to include entity coreference resolution (CR) in translation. We conclude that CR significantly helps pronoun translation and boosts the whole translation quality according to human judgment. Third, we focus on the discourse connections at the sentence level. We propose enhancing the sequential model to infer long-term connections by incorporating a âself-attentionâ mechanism. This mechanism gives direct and selective access to the context. Experiments in different language pairs show that our method outperforms various baselines, and the analysis confirms that the model emphasizes a broader context and captures syntactic-like structures. Fourth, we formulate the problem of document-level NMT and model inter-sentential connections among words with a hierarchical attention mechanism. Experiments on multiple data sets show significant improvement over two strong baselines and conclude that the source and target sidesâ contexts are mutually complementary. This set of results confirms that discourse significantly enhances translation quality, verifying our main thesis objective. Our secondary objective is to improve the CR task by modeling the underlying connections among entities at the document-level. This task is particularly challenging for current neural network models because it requires understanding and reasoning. First, we propose a method to detect entity mentions from partially annotated data. We then proposed to model coreference with a graph of entities encoded in a pre-trained language model as an internal structure. The experiments show that these methods outperform various baselines. CR has the potential to help MT and other text generation tasks by maintaining coherence between the entity mentions.

EPFL2021

Machine Translation with Many Manually Labeled Discourse Connectives

Thomas Meyer

The paper presents machine translation experiments from English to Czech with a large amount of manually annotated discourse connectives. The gold-standard discourse relation annotation leads to better translation performance in ranges of 4–60% for some ambiguous English connectives and helps to find correct syntactical constructs in Czech for less ambiguous connectives. Automatic scoring confirms the stability of the newly built discourse-aware translation systems. Error analysis and human translation evaluation point to the cases where the annotation was most and where less helpful.

2013

Discourse-level features for statistical machine translation

Thomas Meyer

EPFL2015

Discourse Phenomena in Machine Translation

Lesly Sadiht Miculicich Werlen

EPFL2021