Vocabulary Selection Strategies for Neural Machine Translation
Classical translation models constrain the space of possible outputs by selecting a subset of translation rules based on the input sentence. Recent work on improving the efficiency of neural translation models adopted a similar strategy by restricting the output vocabulary to a subset of likely candidates given the source. In this paper we experiment with context and embedding-based selection methods and extend previous work by examining speed and accuracy trade-offs in more detail. We show that decoding time on CPUs can be reduced by up to 90% and training time by 25% on the WMT15 English-German and WMT16 English-Romanian tasks at the same or only negligible change in accuracy. This brings the time to decode with a state of the art neural translation system to just over 140 words per seconds on a single CPU core for English-German.
This paper conducts a comprehensive series of experiments on vocabulary selection strategies to reduce the computational cost of neural machine translation. A range of techniques are investigated, ranging from very simple methods such as word co-occurences, to the relatively complex use of SVMs. The experiments are solid, comprehensive and very useful in practical terms. It is good to see that the best vocabulary selection method is very effective at achieving a very high proportion of the coverage of the full-vocabulary model (fig 3). However, I feel that the experiments in section 4.3 (vocabulary selection during training) was rather limited in their scope - I would have liked to see more experiments here. A major criticism I have with this paper is that there is little novelty here. The techniques are mostly standard methods and rather simple, and in particular, there it seems that there is not much additional material beyond the work of Mi et al (2016). So although the work is solid, the lack of originality lets it down. Minor comments: in 2.1, the word co-occurence measure - was any smoothing used to make this measure more robust to low counts?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Vocabulary Selection Strategies for Neural Machine Translation
Classical translation models constrain the space of possible outputs by selecting a subset of translation rules based on the input sentence. Recent work on improving the efficiency of neural translation models adopted a similar strategy by restricting the output vocabulary to a subset of likely candidates given the source. In this paper we experiment with context and embedding-based selection methods and extend previous work by examining speed and accuracy trade-offs in more detail. We show that decoding time on CPUs can be reduced by up to 90% and training time by 25% on the WMT15 English-German and WMT16 English-Romanian tasks at the same or only negligible change in accuracy. This brings the time to decode with a state of the art neural translation system to just over 140 words per seconds on a single CPU core for English-German.
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<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 conducts a comprehensive series of experiments on vocabulary selection strategies to reduce the computational cost of neural machine translation. A range of techniques are investigated, ranging from very simple methods such as word co-occurences, to the relatively complex use of SVMs. The experiments are solid, comprehensive and very useful in practical terms. It is good to see that the best vocabulary selection method is very effective at achieving a very high proportion of the coverage of the full-vocabulary model (fig 3). However, I feel that the experiments in section 4.3 (vocabulary selection during training) was rather limited in their scope - I would have liked to see more experiments here. A major criticism I have with this paper is that there is little novelty here. The techniques are mostly standard methods and rather simple, and in particular, there it seems that there is not much additional material beyond the work of Mi et al (2016). So although the work is solid, the lack of originality lets it down. Minor comments: in 2.1, the word co-occurence measure - was any smoothing used to make this measure more robust to low counts?
assistant
This paper conducts a comprehensive series of experiments on vocabulary selection strategies to reduce the computational cost of neural machine translation. A range of techniques are investigated, ranging from very simple methods such as word co-occurences, to the relatively complex use of SVMs. The experiments are solid, comprehensive and very useful in practical terms. It is good to see that the best vocabulary selection method is very effective at achieving a very high proportion of the coverage of the full-vocabulary model (fig 3). However, I feel that the experiments in section 4.3 (vocabulary selection during training) was rather limited in their scope - I would have liked to see more experiments here. A major criticism I have with this paper is that there is little novelty here. The techniques are mostly standard methods and rather simple, and in particular, there it seems that there is not much additional material beyond the work of Mi et al (2016). So although the work is solid, the lack of originality lets it down. Minor comments: in 2.1, the word co-occurence measure - was any smoothing used to make this measure more robust to low counts?