Here*s My Point: Argumentation Mining with Pointer Networks
One of the major goals in automated argumentation mining is to uncover the argument structure present in argumentative text. In order to determine this structure, one must understand how different individual components of the overall argument are linked. General consensus in this field dictates that the argument components form a hierarchy of persuasion, which manifests itself in a tree structure. This work provides the first neural network-based approach to argumentation mining, focusing on extracting links between argument components, with a secondary focus on classifying types of argument components. In order to solve this problem, we propose to use a modification of a Pointer Network architecture. A Pointer Network is appealing for this task for the following reasons: 1) It takes into account the sequential nature of argument components; 2) By construction, it enforces certain properties of the tree structure present in argument relations; 3) The hidden representations can be applied to auxiliary tasks. In order to extend the contribution of the original Pointer Network model, we construct a joint model that simultaneously attempts to learn the type of argument component, as well as continuing to predict links between argument components. The proposed model achieves state-of-the-art results on two separate evaluation corpora. Furthermore, our results show that optimizing for both tasks, as well as adding a fully-connected layer prior to recurrent neural network input, is crucial for high performance.
This paper proposes a model for the task of argumentation mining (labeling the set of relationships between statements expressed as sentence-sized spans in a short text). The model combines a pointer network component that identifies links between statements and a classifier that predicts the roles of these statements. The resulting model works well: It outperforms strong baselines, even on datasets with fewer than 100 training examples. I don*t see any major technical issues with this paper, and the results are strong. I am concerned, though, that the paper doesn*t make a substantial novel contribution to representation learning. It focuses on ways to adapt reasonably mature techniques to a novel NLP problem. I think that one of the ACL conferences would be a better fit for this work. The choice of a pointer network for this problem seems reasonable, though (as noted by other commenters) the paper does not make any substantial comparison with other possible ways of producing trees. The paper does a solid job at breaking down the results quantitatively, but I would appreciate some examples of model output and some qualitative error analysis. Detail notes: - Figure 2 appears to have an error. You report that the decoder produces a distribution over input indices only, but you show an example of the network pointing to an output index in one case. - I don*t think *Wei12* is a name.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Here*s My Point: Argumentation Mining with Pointer Networks
One of the major goals in automated argumentation mining is to uncover the argument structure present in argumentative text. In order to determine this structure, one must understand how different individual components of the overall argument are linked. General consensus in this field dictates that the argument components form a hierarchy of persuasion, which manifests itself in a tree structure. This work provides the first neural network-based approach to argumentation mining, focusing on extracting links between argument components, with a secondary focus on classifying types of argument components. In order to solve this problem, we propose to use a modification of a Pointer Network architecture. A Pointer Network is appealing for this task for the following reasons: 1) It takes into account the sequential nature of argument components; 2) By construction, it enforces certain properties of the tree structure present in argument relations; 3) The hidden representations can be applied to auxiliary tasks. In order to extend the contribution of the original Pointer Network model, we construct a joint model that simultaneously attempts to learn the type of argument component, as well as continuing to predict links between argument components. The proposed model achieves state-of-the-art results on two separate evaluation corpora. Furthermore, our results show that optimizing for both tasks, as well as adding a fully-connected layer prior to recurrent neural network input, is crucial for high performance.
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 proposes a model for the task of argumentation mining (labeling the set of relationships between statements expressed as sentence-sized spans in a short text). The model combines a pointer network component that identifies links between statements and a classifier that predicts the roles of these statements. The resulting model works well: It outperforms strong baselines, even on datasets with fewer than 100 training examples. I don*t see any major technical issues with this paper, and the results are strong. I am concerned, though, that the paper doesn*t make a substantial novel contribution to representation learning. It focuses on ways to adapt reasonably mature techniques to a novel NLP problem. I think that one of the ACL conferences would be a better fit for this work. The choice of a pointer network for this problem seems reasonable, though (as noted by other commenters) the paper does not make any substantial comparison with other possible ways of producing trees. The paper does a solid job at breaking down the results quantitatively, but I would appreciate some examples of model output and some qualitative error analysis. Detail notes: - Figure 2 appears to have an error. You report that the decoder produces a distribution over input indices only, but you show an example of the network pointing to an output index in one case. - I don*t think *Wei12* is a name.
assistant
This paper proposes a model for the task of argumentation mining (labeling the set of relationships between statements expressed as sentence-sized spans in a short text). The model combines a pointer network component that identifies links between statements and a classifier that predicts the roles of these statements. The resulting model works well: It outperforms strong baselines, even on datasets with fewer than 100 training examples. I don*t see any major technical issues with this paper, and the results are strong. I am concerned, though, that the paper doesn*t make a substantial novel contribution to representation learning. It focuses on ways to adapt reasonably mature techniques to a novel NLP problem. I think that one of the ACL conferences would be a better fit for this work. The choice of a pointer network for this problem seems reasonable, though (as noted by other commenters) the paper does not make any substantial comparison with other possible ways of producing trees. The paper does a solid job at breaking down the results quantitatively, but I would appreciate some examples of model output and some qualitative error analysis. Detail notes: - Figure 2 appears to have an error. You report that the decoder produces a distribution over input indices only, but you show an example of the network pointing to an output index in one case. - I don*t think *Wei12* is a name.