Task: Language Identification
Input (Spanish, English): - Strengths: A well written paper, examining the use of context in lexical entailment task is a great idea, a well defined approach and experimental set-up and good analysis of the results   - Weaknesses: Some information is missing or insufficient, e.g., the table captions should be more descriptive, a clear description for each of the word type features should be given.  General Discussion:   The paper presents a proposal of consideration of context in lexical entailment task. The results from the experiments demonstrate that context-informed models do better than context-agnostic models on the entailment task.   I liked the idea of creating negative examples to get negative annotations automatically in the two ways described in the paper based on WordNet positive examples. (new dataset; an interesting method to develop dataset)  I also liked the idea of transforming already-used context-agnostic representations into contextualized representations, experimenting with different ways to get contextualized representations (i.e., mask vs contetx2vec), and testing the model on 3 different datasets (generalizability not just across different datasets but also cross-linguistically).  Motivations for various decisions in the experimental design were good to see, e.g., why authors used the split they used for CONTEXT-PPDB (it showed that they thought out clearly what exactly they were doing and why).  Lines 431-434: authors might want to state briefly how the class weights were determined and added to account for the unbalanced data in the CONTEXT-WN experiments. Would it affect direct comparisons with previous work, in what ways?   Change in Line 589: directionality 4 --> directionality, as in Table 4  Suggested change in Line 696-697: is-a hierarchy of WordNet --> "is-a" hierarchy of WordNet   For the sake of completeness, represent "mask" also in Figure 1.  I have read the author response.
Output (English): en