Deep Biaffine Attention for Neural Dependency Parsing
This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7% UAS and 94.1% LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmark—outperforming Kiperwasser & Goldberg (2016) by 1.8% and 2.2%—and comparable to the highest performing transition-based parser (Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show which hyperparameter choices had a significant effect on parsing accuracy, allowing us to achieve large gains over other graph-based approaches.
The paper proposes a new function for computing arc score between two words in a sentence for dependency parsing. The proposed function is biaffine in the sense that it*s a combination of a bilinear score function and a bias term playing a role as prior. The paper reports new state-of-the-art dependency parsing performances on both English PTB and Chinese TB. The paper is very well written with impressive experimental results and analysis. However, the idea is hardly novel regarding to the theme of the conference: the framework that the paper uses is from Kiperwasser & Goldberg (2016), the use of bilinear score function for attention is from Luong et al (2015). Projecting BiLSTM outputs into different spaces using MLPs is a trivial step to make the model *deeper*, whereas adding linear bias terms isn*t confirmed to work in the experiments (table 2 shows that diag bilinear has a close performance to biaffine). I think that this paper is more proper for NLP conferences.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Biaffine Attention for Neural Dependency Parsing
This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7% UAS and 94.1% LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmark—outperforming Kiperwasser & Goldberg (2016) by 1.8% and 2.2%—and comparable to the highest performing transition-based parser (Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show which hyperparameter choices had a significant effect on parsing accuracy, allowing us to achieve large gains over other graph-based approaches.
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 paper proposes a new function for computing arc score between two words in a sentence for dependency parsing. The proposed function is biaffine in the sense that it*s a combination of a bilinear score function and a bias term playing a role as prior. The paper reports new state-of-the-art dependency parsing performances on both English PTB and Chinese TB. The paper is very well written with impressive experimental results and analysis. However, the idea is hardly novel regarding to the theme of the conference: the framework that the paper uses is from Kiperwasser & Goldberg (2016), the use of bilinear score function for attention is from Luong et al (2015). Projecting BiLSTM outputs into different spaces using MLPs is a trivial step to make the model *deeper*, whereas adding linear bias terms isn*t confirmed to work in the experiments (table 2 shows that diag bilinear has a close performance to biaffine). I think that this paper is more proper for NLP conferences.
assistant
The paper proposes a new function for computing arc score between two words in a sentence for dependency parsing. The proposed function is biaffine in the sense that it*s a combination of a bilinear score function and a bias term playing a role as prior. The paper reports new state-of-the-art dependency parsing performances on both English PTB and Chinese TB. The paper is very well written with impressive experimental results and analysis. However, the idea is hardly novel regarding to the theme of the conference: the framework that the paper uses is from Kiperwasser & Goldberg (2016), the use of bilinear score function for attention is from Luong et al (2015). Projecting BiLSTM outputs into different spaces using MLPs is a trivial step to make the model *deeper*, whereas adding linear bias terms isn*t confirmed to work in the experiments (table 2 shows that diag bilinear has a close performance to biaffine). I think that this paper is more proper for NLP conferences.