Few-shot Text Classification with Distributional Signatures
In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging–lexical features highly informative for one task maybe insignificant for another. Thus, rather than learning solely from words, our model also leverages their distributional signatures, which encode pertinent word occurrence patterns. Our model is trained within a meta-learning framework to map these signatures into attention scores, which are then used to weight the lexical representations of words. We demonstrate that our model consistently outperforms prototypical networks learned on lexical knowledge (Snell et al., 2017) in both few-shot text classification and relation classification by a significant margin across six benchmark datasets (19.96% on average in 1-shot classification).
UPDATE: Based on the extensive improvements by the authors, I have updated my rating. However, I still have doubts about the potential of this approach to reach practically useful levels of accuracy. This paper introduces a simple method to weight pretrained lexical features for use in meta learning of few-shot text classification. The method boils down to weighting word inut features, in the form of pretrained word-embeddings, by attention computed from inverse document frequency and class local mutual information. The idea is that this measure of feature informativeness transfers between tasks, whereas lexical features themselves are highly task-specific. The approach is well motivated and is empirically shown to outperform existing approaches to few-shot text classification with a significant margin. While the improvement over existing approaches is quite substantial, I believe the paper should not be accepted to ICLR for the following reasons. First, the contribution is quite limited and not particularly novel. While two weight functions are proposed, the majority of improvement comes simply from normalizing IDF with attention. Based on existing work on delexicalized features for NLP tasks such as parsing, this is quite a straightforward extension. Given the limited contribution, a short paper seems a better fit. Second, the approach simply trades variance for bias. This brings us to the question of how likely the approach is to be a building block in bringing us towards a pratically useful few-shot classification method. Given the weak representational power of the model, I believe this is unlikely. I see a situation similar to syntactic parsing for low-resource languages, where a collection of simple techniques similar in spirit to the current approach, like delexicalization, brought results far above the naive baselines, but never approached practically useful results. I think this is a crucial point to address in meta-learning research in general to make sure we’re not just solving a toy problem with tailored heuristics. Additional notes: What is the motivation for using a BiLSTM to combine inverse document frequency and inverse class entropy? Is the sequence information at all useful, or would a simple projection and nonlinearity give the same result? The theoretical analysis is completely self-evident from the definition of the feature space. Replaing a feature with an equivalent feature of course gives the same result and I don’t see the need to “mathematize” this. The effect of approximating logistic regression with linear regression + calibration is not analyzed and it is not clear what the effect of this approximation is in the text classification scenario. I would suggest to compare to differentiating through a direct optimization of the logistic formulation, for example with Newton’s method, or plain SGD, as in Bertinetto et al. (2019). Table 1. Why not run the attention-based feature aggregator together with all algorithms. The main contribution is at the input representation level, and this should be applicable across algorithms. In fact, if we remove the BiLSTM which seems to have a very small effect the representation function does not contain learnable parameters in itself. Please provide the average across datasets in Table 1.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Few-shot Text Classification with Distributional Signatures
In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging–lexical features highly informative for one task maybe insignificant for another. Thus, rather than learning solely from words, our model also leverages their distributional signatures, which encode pertinent word occurrence patterns. Our model is trained within a meta-learning framework to map these signatures into attention scores, which are then used to weight the lexical representations of words. We demonstrate that our model consistently outperforms prototypical networks learned on lexical knowledge (Snell et al., 2017) in both few-shot text classification and relation classification by a significant margin across six benchmark datasets (19.96% on average in 1-shot classification).
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>



UPDATE: Based on the extensive improvements by the authors, I have updated my rating. However, I still have doubts about the potential of this approach to reach practically useful levels of accuracy. This paper introduces a simple method to weight pretrained lexical features for use in meta learning of few-shot text classification. The method boils down to weighting word inut features, in the form of pretrained word-embeddings, by attention computed from inverse document frequency and class local mutual information. The idea is that this measure of feature informativeness transfers between tasks, whereas lexical features themselves are highly task-specific. The approach is well motivated and is empirically shown to outperform existing approaches to few-shot text classification with a significant margin. While the improvement over existing approaches is quite substantial, I believe the paper should not be accepted to ICLR for the following reasons. First, the contribution is quite limited and not particularly novel. While two weight functions are proposed, the majority of improvement comes simply from normalizing IDF with attention. Based on existing work on delexicalized features for NLP tasks such as parsing, this is quite a straightforward extension. Given the limited contribution, a short paper seems a better fit. Second, the approach simply trades variance for bias. This brings us to the question of how likely the approach is to be a building block in bringing us towards a pratically useful few-shot classification method. Given the weak representational power of the model, I believe this is unlikely. I see a situation similar to syntactic parsing for low-resource languages, where a collection of simple techniques similar in spirit to the current approach, like delexicalization, brought results far above the naive baselines, but never approached practically useful results. I think this is a crucial point to address in meta-learning research in general to make sure we’re not just solving a toy problem with tailored heuristics. Additional notes: What is the motivation for using a BiLSTM to combine inverse document frequency and inverse class entropy? Is the sequence information at all useful, or would a simple projection and nonlinearity give the same result? The theoretical analysis is completely self-evident from the definition of the feature space. Replaing a feature with an equivalent feature of course gives the same result and I don’t see the need to “mathematize” this. The effect of approximating logistic regression with linear regression + calibration is not analyzed and it is not clear what the effect of this approximation is in the text classification scenario. I would suggest to compare to differentiating through a direct optimization of the logistic formulation, for example with Newton’s method, or plain SGD, as in Bertinetto et al. (2019). Table 1. Why not run the attention-based feature aggregator together with all algorithms. The main contribution is at the input representation level, and this should be applicable across algorithms. In fact, if we remove the BiLSTM which seems to have a very small effect the representation function does not contain learnable parameters in itself. Please provide the average across datasets in Table 1.
assistant
UPDATE: Based on the extensive improvements by the authors, I have updated my rating. However, I still have doubts about the potential of this approach to reach practically useful levels of accuracy. This paper introduces a simple method to weight pretrained lexical features for use in meta learning of few-shot text classification. The method boils down to weighting word inut features, in the form of pretrained word-embeddings, by attention computed from inverse document frequency and class local mutual information. The idea is that this measure of feature informativeness transfers between tasks, whereas lexical features themselves are highly task-specific. The approach is well motivated and is empirically shown to outperform existing approaches to few-shot text classification with a significant margin. While the improvement over existing approaches is quite substantial, I believe the paper should not be accepted to ICLR for the following reasons. First, the contribution is quite limited and not particularly novel. While two weight functions are proposed, the majority of improvement comes simply from normalizing IDF with attention. Based on existing work on delexicalized features for NLP tasks such as parsing, this is quite a straightforward extension. Given the limited contribution, a short paper seems a better fit. Second, the approach simply trades variance for bias. This brings us to the question of how likely the approach is to be a building block in bringing us towards a pratically useful few-shot classification method. Given the weak representational power of the model, I believe this is unlikely. I see a situation similar to syntactic parsing for low-resource languages, where a collection of simple techniques similar in spirit to the current approach, like delexicalization, brought results far above the naive baselines, but never approached practically useful results. I think this is a crucial point to address in meta-learning research in general to make sure we’re not just solving a toy problem with tailored heuristics. Additional notes: What is the motivation for using a BiLSTM to combine inverse document frequency and inverse class entropy? Is the sequence information at all useful, or would a simple projection and nonlinearity give the same result? The theoretical analysis is completely self-evident from the definition of the feature space. Replaing a feature with an equivalent feature of course gives the same result and I don’t see the need to “mathematize” this. The effect of approximating logistic regression with linear regression + calibration is not analyzed and it is not clear what the effect of this approximation is in the text classification scenario. I would suggest to compare to differentiating through a direct optimization of the logistic formulation, for example with Newton’s method, or plain SGD, as in Bertinetto et al. (2019). Table 1. Why not run the attention-based feature aggregator together with all algorithms. The main contribution is at the input representation level, and this should be applicable across algorithms. In fact, if we remove the BiLSTM which seems to have a very small effect the representation function does not contain learnable parameters in itself. Please provide the average across datasets in Table 1.