EFFICIENT SEQUENCE LABELING WITH ACTOR-CRITIC TRAINING
Neural approaches to sequence labeling often use a Conditional Random Field (CRF) to model their output dependencies, while Recurrent Neural Networks (RNN) are used for the same purpose in other tasks. We set out to establish RNNs as an attractive alternative to CRFs for sequence labeling. To do so, we address one of the RNN’s most prominent shortcomings, the fact that it is not exposed to its own errors with the maximum-likelihood training. We frame the prediction of the output sequence as a sequential decision-making process, where we train the network with an adjusted actor-critic algorithm (AC-RNN). We comprehensively compare this strategy with maximum-likelihood training for both RNNs and CRFs on three structured-output tasks. The proposed AC-RNN efficiently matches the performance of the CRF on NER and CCG tagging, and outperforms it on Machine Transliteration. We also show that our training strategy is significantly better than other techniques for addressing RNN’s exposure bias, such as Scheduled Sampling, and Self-Critical policy training.
 The authors propose actor-critic method for sequence labeling and show that it performs better (is more stable than) other RL approaches and also outperforms other techniques for countering exposure bias like scheduled sampling. The results show very small improvement in tagging tasks like NER and CCG supertagging compared to other approaches ; but they show good improvement in the transliteration task which is more of a transduction task than a tagging task. This authors also discus the adjusted training procedure which accounts for bad performance of the critic model in the initial stages of training. The approach is not very novel because actor-critic for more general sequence-to-sequence models (arguably more complex than tagging) has already been explored in the literature (Bahdanau et al., cited by the authors). Major difference in the proposed approach is the use of stepwise hamming-loss based reward and it is unclear whether this is a major contribution which sets it apart from the previous work on AC for sequence modeling. For example, a good comparison would be to do tagging in seq2seq style and use the approach proposed in the existing AC work to show the value of the approach proposed here. Also, minor claims about thoroughness of comparison with CRF are ill-founded as previous published work on tagging has indeed compared CRFs, independent, LSTM/RNN based models.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
EFFICIENT SEQUENCE LABELING WITH ACTOR-CRITIC TRAINING
Neural approaches to sequence labeling often use a Conditional Random Field (CRF) to model their output dependencies, while Recurrent Neural Networks (RNN) are used for the same purpose in other tasks. We set out to establish RNNs as an attractive alternative to CRFs for sequence labeling. To do so, we address one of the RNN’s most prominent shortcomings, the fact that it is not exposed to its own errors with the maximum-likelihood training. We frame the prediction of the output sequence as a sequential decision-making process, where we train the network with an adjusted actor-critic algorithm (AC-RNN). We comprehensively compare this strategy with maximum-likelihood training for both RNNs and CRFs on three structured-output tasks. The proposed AC-RNN efficiently matches the performance of the CRF on NER and CCG tagging, and outperforms it on Machine Transliteration. We also show that our training strategy is significantly better than other techniques for addressing RNN’s exposure bias, such as Scheduled Sampling, and Self-Critical policy training.
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>



 The authors propose actor-critic method for sequence labeling and show that it performs better (is more stable than) other RL approaches and also outperforms other techniques for countering exposure bias like scheduled sampling. The results show very small improvement in tagging tasks like NER and CCG supertagging compared to other approaches ; but they show good improvement in the transliteration task which is more of a transduction task than a tagging task. This authors also discus the adjusted training procedure which accounts for bad performance of the critic model in the initial stages of training. The approach is not very novel because actor-critic for more general sequence-to-sequence models (arguably more complex than tagging) has already been explored in the literature (Bahdanau et al., cited by the authors). Major difference in the proposed approach is the use of stepwise hamming-loss based reward and it is unclear whether this is a major contribution which sets it apart from the previous work on AC for sequence modeling. For example, a good comparison would be to do tagging in seq2seq style and use the approach proposed in the existing AC work to show the value of the approach proposed here. Also, minor claims about thoroughness of comparison with CRF are ill-founded as previous published work on tagging has indeed compared CRFs, independent, LSTM/RNN based models.
assistant
 The authors propose actor-critic method for sequence labeling and show that it performs better (is more stable than) other RL approaches and also outperforms other techniques for countering exposure bias like scheduled sampling. The results show very small improvement in tagging tasks like NER and CCG supertagging compared to other approaches ; but they show good improvement in the transliteration task which is more of a transduction task than a tagging task. This authors also discus the adjusted training procedure which accounts for bad performance of the critic model in the initial stages of training. The approach is not very novel because actor-critic for more general sequence-to-sequence models (arguably more complex than tagging) has already been explored in the literature (Bahdanau et al., cited by the authors). Major difference in the proposed approach is the use of stepwise hamming-loss based reward and it is unclear whether this is a major contribution which sets it apart from the previous work on AC for sequence modeling. For example, a good comparison would be to do tagging in seq2seq style and use the approach proposed in the existing AC work to show the value of the approach proposed here. Also, minor claims about thoroughness of comparison with CRF are ill-founded as previous published work on tagging has indeed compared CRFs, independent, LSTM/RNN based models.