HUME: Human UCCA-Based Evaluation of Machine Translation

Human evaluation of machine translation normally uses sentence-level measures such as relative ranking or adequacy scales. However, these provide no insight into possible errors, and do not scale well with sentence length. We argue for a semantics-based evaluation, which captures what meaning components are retained in the MT output, thus providing a more fine-grained analysis of translation quality, and enabling the construction and tuning of semantics-based MT. We present a novel human semantic evaluation measure, Human UCCA-based MT Evaluation (HUME), building on the UCCA semantic representation scheme. HUME covers a wider range of semantic phenomena than previous methods and does not rely on semantic annotation of the potentially garbled MT output. We experiment with four language pairs, demonstrating HUME's broad applicability, and report good inter-annotator agreement rates and correlation with human adequacy scores

The role of syntax and semantics in machine translation and quality estimation of machine-translated user-generated content

The availability of the Internet has led to a steady increase in the volume of online user-generated content, the majority of which is in English. Machine-translating this content to other languages can help disseminate the information contained in it to a broader audience. However, reliably publishing these translations requires a prior estimate of their quality. This thesis is concerned with the statistical machine translation of Symantec's Norton forum content, focusing in particular on its quality estimation (QE) using syntactic and semantic information. We compare the output of phrase-based and syntax-based English-to-French and English-to-German machine translation (MT) systems automatically and manually, and nd that the syntax-based methods do not necessarily handle grammar-related phenomena in translation better than the phrase-based methods. Although these systems generate suciently dierent outputs, the apparent lack of a systematic dierence between these outputs impedes its utilisation in a combination framework. To investigate the role of syntax and semantics in quality estimation of machine translation, we create SymForum, a data set containing French machine translations of English sentences from Norton forum content, their post-edits and their adequacy and uency scores. We use syntax in quality estimation via tree kernels, hand-crafted features and their combination, and nd it useful both alone and in combination with surface-driven features. Our analyses show that neither the accuracy of the syntactic parses used by these systems nor the parsing quality of the MT output aect QE performance. We also nd that adding more structure to French Treebank parse trees can be useful for syntax-based QE. We use semantic role labelling (SRL) for our semantic-based QE experiments. We experiment with the limited resources that are available for French and nd that a small manually annotated training set is substantially more useful than a much larger articially created set. We use SRL in quality estimation using tree kernels, hand-crafted features and their combination. Additionally, we introduce PAM, a QE metric based on the predicate-argument structure match between source and target. We nd that the SRL quality, especially on the target side, is the major factor negatively aecting the performance of the semantic-based QE. Finally, we annotate English and French Norton forum sentences with their phrase structure syntax using an annotation strategy adapted for user-generated text. We nd that user errors occur in only a small fraction of the data, but their correction does improve parsing performance. These treebanks (Foreebank) prove to be useful as supplementary training data in adapting the parsers to the forum text. The improved parses ultimately increase the performance of the semantic-based QE. However, a reliable semantic-based QE system requires further improvements in the quality of the underlying semantic role labelling

The mat sat on the cat : investigating structure in the evaluation of order in machine translation

We present a multifaceted investigation into the relevance of word order in machine translation. We introduce two tools, DTED and DERP, each using dependency structure to detect differences between the structures of machine-produced translations and human-produced references. DTED applies the principle of Tree Edit Distance to calculate edit operations required to convert one structure into another. Four variants of DTED have been produced, differing in the importance they place on words which match between the two sentences. DERP represents a more detailed procedure, making use of the dependency relations between words when evaluating the disparities between paths connecting matching nodes. In order to empirically evaluate DTED and DERP, and as a standalone contribution, we have produced WOJ-DB, a database of human judgments. Containing scores relating to translation adequacy and more specifically to word order quality, this is intended to support investigations into a wide range of translation phenomena. We report an internal evaluation of the information in WOJ-DB, then use it to evaluate variants of DTED and DERP, both to determine their relative merit and their strength relative to third-party baselines. We present our conclusions about the importance of structure to the tools and their relevance to word order specifically, then propose further related avenues of research suggested or enabled by our work