- 071: This formulation of argumentation mining is just one of several proposed subtask divisions, and this should be mentioned. For example, in [1], claims are detected and classified before any supporting evidence is detected.
Furthermore, [2] applied neural networks to this task, so it is inaccurate to say (as is claimed in the abstract of this paper) that this work is the first NN-based approach to argumentation mining.
- Two things must be improved in the presentation of the model: (1) What is the pooling method used for embedding features (line 397)? and (2) Equation (7) in line 472 is not clear enough: is E_i the random variable representing the *type* of AC i, or its *identity*? Both are supposedly modeled (the latter by feature representation), and need to be defined. Furthermore, it seems like the LHS of equation (7) should be a conditional probability.
- There are several unclear things about Table 2: first, why are the three first baselines evaluated only by macro f1 and the individual f1 scores are missing?
This is not explained in the text. Second, why is only the "PN" model presented? Is this the same PN as in Table 1, or actually the Joint Model? What about the other three?
- It is not mentioned which dataset the experiment described in Table 4 was performed on.
General Discussion: - 132: There has to be a lengthier introduction to pointer networks, mentioning recurrent neural networks in general, for the benefit of readers unfamiliar with "sequence-to-sequence models". Also, the citation of Sutskever et al. (2014) in line 145 should be at the first mention of the term, and the difference with respect to recursive neural networks should be explained before the paragraph starting in line 233 (tree structure etc.).
- 348: The elu activation requires an explanation and citation (still not enough well-known).
- 501: "MC", "Cl" and "Pr" should be explained in the label.
- 577: A sentence about how these hyperparameters were obtained would be appropriate.
- 590: The decision to do early stopping only by link prediction accuracy should be explained (i.e. why not average with type accuracy, for example?).
- 594: Inference at test time is briefly explained, but would benefit from more details.
- 617: Specify what the length of an AC is measured in (words?).
- 644: The referent of "these" in "Neither of these" is unclear.
- 684: "Minimum" should be "Maximum".
- 694: The performance w.r.t. the amount of training data is indeed surprising, but other models have also achieved almost the same results - this is especially surprising because NNs usually need more data. It would be good to say this.
- 745: This could alternatively show that structural cues are less important for this task.
- Some minor typos should be corrected (e.g. "which is show", line 161).
[1] Rinott, Ruty, et al. "Show Me Your Evidence-an Automatic Method for Context Dependent Evidence Detection." EMNLP. 2015.
[2] Laha, Anirban, and Vikas Raykar. " An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks." COLING. 2016.
- Two things must be improved in the presentation of the model: (1) What is the pooling method used for embedding features (line 397)? and (2) Equation (7) in line 472 is not clear enough: is E_i the random variable representing the *type* of AC i, or its *identity*? Both are supposedly modeled (the latter by feature representation), and need to be defined. Furthermore, it seems like the LHS of equation (7) should be a conditional probability.