Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation
Automated metrics such as BLEU are widely used in the machine translation literature. They have also been used recently in the dialogue community for evaluating dialogue response generation. However, previous work in dialogue response generation has shown that these metrics do not correlate strongly with human judgment in the non task-oriented dialogue setting. Task-oriented dialogue responses are expressed on narrower domains and exhibit lower diversity. It is thus reasonable to think that these automated metrics would correlate well with human judgment in the task-oriented setting where the generation task consists of translating dialogue acts into a sentence. We conduct an empirical study to confirm whether this is the case. Our findings indicate that these automated metrics have stronger correlation with human judgments in the task-oriented setting compared to what has been observed in the non task-oriented setting. We also observe that these metrics correlate even better for datasets which provide multiple ground truth reference sentences. In addition, we show that some of the currently available corpora for task-oriented language generation can be solved with simple models and advocate for more challenging datasets.
 This paper*s main thesis is that automatic metrics like BLEU, ROUGE, or METEOR is suitable for task-oriented natural language generation (NLG). In particular, the paper presents a counterargument to "How NOT To Evaluate Your Dialogue System..." where Wei et al argue that automatic metrics are not correlated or only weakly correlated with human eval on dialogue generation. The authors here show that the performance of various NN models as measured by automatic metrics like BLEU and METEOR is correlated with human eval. Overall, this paper presents a useful conclusion: use METEOR for evaluating task oriented NLG. However, there isn*t enough novel contribution in this paper to warrant a publication. Many of the details unnecessary: 1) various LSTM model descriptions are unhelpful given the base LSTM model does just as well on the presented tasks 2) Many embedding based eval methods are proposed but no conclusions are drawn from any of these techniques.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation
Automated metrics such as BLEU are widely used in the machine translation literature. They have also been used recently in the dialogue community for evaluating dialogue response generation. However, previous work in dialogue response generation has shown that these metrics do not correlate strongly with human judgment in the non task-oriented dialogue setting. Task-oriented dialogue responses are expressed on narrower domains and exhibit lower diversity. It is thus reasonable to think that these automated metrics would correlate well with human judgment in the task-oriented setting where the generation task consists of translating dialogue acts into a sentence. We conduct an empirical study to confirm whether this is the case. Our findings indicate that these automated metrics have stronger correlation with human judgments in the task-oriented setting compared to what has been observed in the non task-oriented setting. We also observe that these metrics correlate even better for datasets which provide multiple ground truth reference sentences. In addition, we show that some of the currently available corpora for task-oriented language generation can be solved with simple models and advocate for more challenging datasets.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper*s main thesis is that automatic metrics like BLEU, ROUGE, or METEOR is suitable for task-oriented natural language generation (NLG). In particular, the paper presents a counterargument to "How NOT To Evaluate Your Dialogue System..." where Wei et al argue that automatic metrics are not correlated or only weakly correlated with human eval on dialogue generation. The authors here show that the performance of various NN models as measured by automatic metrics like BLEU and METEOR is correlated with human eval. Overall, this paper presents a useful conclusion: use METEOR for evaluating task oriented NLG. However, there isn*t enough novel contribution in this paper to warrant a publication. Many of the details unnecessary: 1) various LSTM model descriptions are unhelpful given the base LSTM model does just as well on the presented tasks 2) Many embedding based eval methods are proposed but no conclusions are drawn from any of these techniques.
assistant
 This paper*s main thesis is that automatic metrics like BLEU, ROUGE, or METEOR is suitable for task-oriented natural language generation (NLG). In particular, the paper presents a counterargument to "How NOT To Evaluate Your Dialogue System..." where Wei et al argue that automatic metrics are not correlated or only weakly correlated with human eval on dialogue generation. The authors here show that the performance of various NN models as measured by automatic metrics like BLEU and METEOR is correlated with human eval. Overall, this paper presents a useful conclusion: use METEOR for evaluating task oriented NLG. However, there isn*t enough novel contribution in this paper to warrant a publication. Many of the details unnecessary: 1) various LSTM model descriptions are unhelpful given the base LSTM model does just as well on the presented tasks 2) Many embedding based eval methods are proposed but no conclusions are drawn from any of these techniques.