Improving the objective function in minimum error rate training

In Minimum Error Rate Training (MERT), the parameters of an SMT system are tuned on a certain evaluation metric to improve translation quality. In this paper, we present empirical results in which parameters tuned on one metric (e.g. BLEU) may not lead to optimal scores on the same metric. The score can be improved significantly by tuning on an entirely different metric (e.g. METEOR, by 0.82 BLEU points or 3.38% relative improvement on WMT08 English–French dataset). We analyse the impact of choice of objective function in MERT and further propose three combination strategies of different metrics to reduce the bias of a single metric, and obtain parameters that receive better scores (0.99 BLEU points or 4.08% relative improvement) on evaluation metrics than those tuned on the standalone metric itself

Machine translation evaluation resources and methods: a survey

We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT. This paper differs from the existing works\cite {GALEprogram2009, EuroMatrixProject2007} from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content

Discourse Structure in Machine Translation Evaluation

In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding correlation with human judgments both at the segment- and at the system-level. This suggests that discourse information is complementary to the information used by many of the existing evaluation metrics, and thus it could be taken into account when developing richer evaluation metrics, such as the WMT-14 winning combined metric DiscoTKparty. We also provide a detailed analysis of the relevance of various discourse elements and relations from the RST parse trees for machine translation evaluation. In particular we show that: (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference tree is positively correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse analysis. Computational Linguistics, 201

Source-side context-informed hypothesis alignment for combining outputs from machine translation systems

This paper presents a new hypothesis alignment method for combining outputs of multiple machine translation (MT) systems. Traditional hypothesis alignment algorithms such as TER, HMM and IHMM do not directly utilise the context information of the source side but rather address the alignment issues via the output data itself. In this paper, a source-side context-informed (SSCI) hypothesis alignment method is proposed to carry out the word alignment and word reordering issues. First of all, the source–target word alignment links are produced as the hidden variables by exporting source phrase spans during the translation decoding process. Secondly, a mapping strategy and normalisation model are employed to acquire the 1- to-1 alignment links and build the confusion network (CN). The source-side context-based method outperforms the state-of-the-art TERbased alignment model in our experiments on the WMT09 English-to-French and NIST Chinese-to-English data sets respectively. Experimental results demonstrate that our proposed approach scores consistently among the best results across different data and language pair conditions

Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation

The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that were predicted by another, historic SMT system. A challenge arises by the fact that risk-averse commercial SMT systems deterministically log the most probable translation. The lack of sufficient exploration of the SMT output space seemingly contradicts the theoretical requirements for counterfactual learning. We show that counterfactual learning from deterministic bandit logs is possible nevertheless by smoothing out deterministic components in learning. This can be achieved by additive and multiplicative control variates that avoid degenerate behavior in empirical risk minimization. Our simulation experiments show improvements of up to 2 BLEU points by counterfactual learning from deterministic bandit feedback.Comment: Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017, Copenhagen, Denmar