We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available. Recent works in response generation have adopted metrics from machine translation to compare a model's generated response to a single target response. We show that these metrics correlate very weakly with human judgements in the non-technical Twitter domain, and not at all in the technical Ubuntu domain. We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future development of better automatic evaluation metrics for dialogue systems.
Evaluation of segment-level machine translation metrics is currently hampered by: (1) low inter-annotator agreement levels in human assessments; (2) lack of an effective mechanism for evaluation of translations of equal quality; and (3) lack of methods of significance testing improvements over a baseline. In this paper, we provide solutions to each of these challenges and outline a new human evaluation methodology aimed specifically at assessment of segment-level metrics. We replicate the human evaluation component of WMT-13 and reveal that the current state-of-the-art performance of segment-level metrics is better than previously believed. Three segment-level metrics — METEOR, NLEPOR and SENTBLEUMOSES — are found to correlate with human assessment at a level not significantly outperformed by any other metric in both the individual language pair assessment for Spanish-toEnglish and the aggregated set of 9 language pairs. We argue that the machine translation community is overly reliant on the Bleu machine translation evaluation metric. We show that an improved Bleu score is neither necessary nor sufficient for achieving an actual improvement in translation quality, and give two significant counterexamples to Bleu’s correlation with human judgments of quality. This offers new potential for research which was previously deemed unpromising by an inability to improve upon Bleu scores. Conversational dialogue systems cannot be evaluated in a fully formal manner, because dialogue is heavily dependent on context and current dialogue theory is not precise enough to specify a target output ahead of time. Instead, we evaluate dialogue systems in a semi-formal manner, using human judges to rate the coherence of a conversational character and correlating these judgments with measures extracted from within the system. We present a series of three evaluations of a single conversational character over the course of a year, demonstrating how this kind of evaluation helps bring about an improvement in overall dialogue coherence. There is growing interest in using automatically computed corpus-based evaluation metrics to evaluate Natural Language Generation (NLG) systems, because these are often considerably cheaper than the human-based evaluations which have traditionally been used in NLG. We review previous work on NLG evaluation and on validation of automatic metrics in NLP, and then present the results of two studies of how well some metrics which are popular in other areas of NLP (notably BLEU and ROUGE) correlate with human judgments in the domain of computer-generated weather forecasts. Our results suggest that, at least in this domain, metrics may provide a useful measure of language quality, although the evidence for this is not as strong as we would ideally like to see; however, they do not provide a useful measure of content quality. We also discuss a number of caveats which must be kept in mind when interpreting this and other validation studies.  This paper presents the results of the WMT10 and MetricsMATR10 shared tasks, which included a translation task, a system combination task, and an evaluation task. We conducted a large-scale manual evaluation of 104 machine translation systems and 41 system combination entries. We used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 26 metrics. This year we also investigated increasing the number of human judgments by hiring non-expert annotators through Amazon's Mechanical Turk.  We examine correlations between native speaker judgements on automatically generated German text against automatic evaluation metrics. We look at a number of metrics from the MT and Summarisation communities and find that for a relative ranking task, most automatic metrics perform equally well and have fairly strong correlations to the human judgements. In contrast, on a naturalness judgement task, the General Text Matcher (GTM) tool correlates best overall, although in general, correlation between the human judgements and the automatic metrics was quite weak. This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries Recent years have seen increasing interest in automatic metrics for the evaluation of generation systems. When a system can generate syntactic variation, automatic evaluation becomes more difficult. In this paper, we compare the performance of several automatic evaluation metrics using a corpus of automatically generated paraphrases. We show that these evaluation metrics can at least partially measure adequacy (similarity in meaning), but are not good measures of fluency (syntactic correctness). We make several proposals for improving the evaluation of generation systems that produce variation.