Nitin Madnani

Fluency, Adequacy, or HTER? Exploring Different Human Judgments with a Tunable MT Metric

Automatic Machine Translation (MT) evaluation metrics have traditionally been evaluated by the correlation of the scores they assign to MT output with human judgments of translation performance. Different types of human judgments, such as Fluency, Adequacy, and HTER, measure varying aspects of MT performance that can be captured by automatic MT metrics. We explore these differences through the use of a new tunable MT metric: TER-Plus, which extends the Translation Edit Rate evaluation metric with tunable parameters and the incorporation of morphology, synonymy and paraphrases. TER-Plus was shown to be one of the top metrics in NISTs Metrics MATR 2008 Challenge, having the highest average rank in terms of Pearson and Spearman correlation. Optimizing TER-Plus to different types of human judgments yields significantly improved correlations and meaningful changes in the weight of different types of edits, demonstrating significant differences between the types of human judgments.

Generating phrasal and sentential paraphrases: A survey of data-driven methods

The task of paraphrasing is inherently familiar to speakers of all languages. Moreover, the task of automatically generating or extracting semantic equivalences for the various units of language—words, phrases, and sentences—is an important part of natural language processing (NLP) and is being increasingly employed to improve the performance of several NLP applications. In this article, we attempt to conduct a comprehensive and application-independent survey of data-driven phrasal and sentential paraphrase generation methods, while also conveying an appreciation for the importance and potential use of paraphrases in the field of NLP research. Recent work done in manual and automatic construction of paraphrase corpora is also examined. We also discuss the strategies used for evaluating paraphrase generation techniques and briefly explore some future trends in paraphrase generation.

TER-Plus: paraphrase, semantic, and alignment enhancements to Translation Edit Rate

This paper describes a new evaluation metric, TER-Plus (TERp) for automatic evaluation of machine translation (MT). TERp is an extension of Translation Edit Rate (TER). It builds on the success of TER as an evaluation metric and alignment tool and addresses several of its weaknesses through the use of paraphrases, stemming, synonyms, as well as edit costs that can be automatically optimized to correlate better with various types of human judgments. We present a correlation study comparing TERp to BLEU, METEOR and TER, and illustrate that TERp can better evaluate translation adequacy.

Using Paraphrases for Parameter Tuning in Statistical Machine Translation

Most state-of-the-art statistical machine translation systems use log-linear models, which are defined in terms of hypothesis features and weights for those features. It is standard to tune the feature weights in order to maximize a translation quality metric, using held-out test sentences and their corresponding reference translations. However, obtaining reference translations is expensive. In this paper, we introduce a new full-sentence paraphrase technique, based on English-to-English decoding with an MT system, and we demonstrate that the resulting paraphrases can be used to drastically reduce the number of human reference translations needed for parameter tuning, without a significant decrease in translation quality.

The Hiero Machine Translation System: Extensions, Evaluation, and Analysis

Hierarchical organization is a well known property of language, and yet the notion of hierarchical structure has been largely absent from the best performing machine translation systems in recent community-wide evaluations. In this paper, we discuss a new hierarchical phrase-based statistical machine translation system (Chiang, 2005), presenting recent extensions to the original proposal, new evaluation results in a community-wide evaluation, and a novel technique for fine-grained comparative analysis of MT systems.

Measuring Variability in Sentence Ordering for News Summarization

The issue of sentence ordering is an important one for natural language tasks such as multi-document summarization, yet there has not been a quantitative exploration of the range of acceptable sentence orderings for short texts. We present results of a sentence reordering experiment with three experimental conditions. Our findings indicate a very high degree of variability in the orderings that the eighteen subjects produce. In addition, the variability of reorderings is significantly greater when the initial ordering seen by subjects is different from the original summary. We conclude that evaluation of sentence ordering should use multiple reference orderings. Our evaluation presents several metrics that might prove useful in assessing against multiple references. We conclude with a deeper set of questions: (a) what sorts of independent assessments of quality of the different reference orderings could be made and (b) whether a large enough test set would obviate the need for such independent means of quality assessment.

Predicting Grammaticality on an Ordinal Scale

Automated methods for identifying whether sentences are grammatical have various potential applications (e.g., machine translation, automated essay scoring, computer-assisted language learning). In this work, we construct a statistical model of grammaticality using various linguistic features (e.g., misspelling counts, parser outputs, n-gram language model scores). We also present a new publicly available dataset of learner sentences judged for grammaticality on an ordinal scale. In evaluations, we compare our system to the one from Post (2011) and find that our approach yields state-of-the-art performance.

E-rating Machine Translation

We describe our submissions to the WMT11 shared MT evaluation task: MTeRater and MTeRater-Plus. Both are machine-learned metrics that use features from e-rater®, an automated essay scoring engine designed to assess writing proficiency. Despite using only features from e-rater and without comparing to translations, MTeRater achieves a sentence-level correlation with human rankings equivalent to BLEU. Since MTeRater only assesses fluency, we build a meta-metric, MTeRater-Plus, that incorporates adequacy by combining MTeRater with other MT evaluation metrics and heuristics. This meta-metric has a higher correlation with human rankings than either MTeRater or individual MT metrics alone. However, we also find that e-rater features may not have significant impact on correlation in every case.

Effective Feature Integration for Automated Short Answer Scoring

A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity. Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used. Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.

Getting started on natural language processing with Python

The term Natural Language Processing encompasses a broad set of techniques for automated generation, manipulation and analysis of natural or human languages. Although most NLP techniques inherit largely from Linguistics and Artificial Intelligence, they are also influenced by relatively newer areas such as Machine Learning, Computational Statistics and Cognitive Science. Before we see some examples of NLP techniques, it will be useful to introduce some very basic terminology. Please note that as a side effect of keeping things simple, these definitions may not stand up to strict linguistic scrutiny.