BERT Wears GloVes: Distilling Static Embeddings from Pretrained Contextual Representations
Contextualized word representations such as ELMo and BERT have become the de facto starting point for incorporating pretrained representations for downstream NLP tasks. In these settings, contextual representations have largely made obsolete their static embedding predecessors such as Word2Vec and GloVe. However, static embeddings do have their advantages in that they are straightforward to understand and faster to use. Additionally, embedding analysis methods for static embeddings are far more diverse and mature than those available for their dynamic counterparts. In this work, we introduce simple methods for generating static lookup table embeddings from existing pretrained contextual representations and demonstrate they outperform Word2Vec and GloVe embeddings on a variety of word similarity and word relatedness tasks. In doing so, our results also reveal insights that may be useful for subsequent downstream tasks using our embeddings or the original contextual models. Further, we demonstrate the increased potential for analysis by applying existing approaches for estimating social bias in word embeddings. Our analysis constitutes the most comprehensive study of social bias in contextual word representations (via the proxy of our distilled embeddings) and reveals a number of inconsistencies in current techniques for quantifying social bias in word embeddings. We publicly release our code and distilled word embeddings to support reproducible research and the broader NLP community.
This paper introduces methods for distilling pretrained contextual representation to a static embeddings for the faster use. It proposes subword pooling and context combination, and its contribution is demonstrated with a suite of classic experiments for static word embeddings. My score for this paper is weakly rejected because (1) the motivation of the proposed approach is not clear. The paper title is about “distill”, but I can’t find out what the methods try to distill, what is the objective; (2) the goal of the paper is to obtain a static embedding performing like ELMO or BERT. As we know, the power of the contextual embedding is demonstrated by different NLP downstreaming task like MT, QA and a bunch of text classification tasks. In this paper, I don’t find those experiments; (3) this paper compared the results with word2vec and glove, but not clear which version to compare. And there are some re-embedding approaches that improve word2vec and glove such as “Word Re-Embedding via Manifold Dimensionality Retention”, the table in this re-embedding paper shows different version of glove and some of them are better than the proposed methods distilled from BERT.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
BERT Wears GloVes: Distilling Static Embeddings from Pretrained Contextual Representations
Contextualized word representations such as ELMo and BERT have become the de facto starting point for incorporating pretrained representations for downstream NLP tasks. In these settings, contextual representations have largely made obsolete their static embedding predecessors such as Word2Vec and GloVe. However, static embeddings do have their advantages in that they are straightforward to understand and faster to use. Additionally, embedding analysis methods for static embeddings are far more diverse and mature than those available for their dynamic counterparts. In this work, we introduce simple methods for generating static lookup table embeddings from existing pretrained contextual representations and demonstrate they outperform Word2Vec and GloVe embeddings on a variety of word similarity and word relatedness tasks. In doing so, our results also reveal insights that may be useful for subsequent downstream tasks using our embeddings or the original contextual models. Further, we demonstrate the increased potential for analysis by applying existing approaches for estimating social bias in word embeddings. Our analysis constitutes the most comprehensive study of social bias in contextual word representations (via the proxy of our distilled embeddings) and reveals a number of inconsistencies in current techniques for quantifying social bias in word embeddings. We publicly release our code and distilled word embeddings to support reproducible research and the broader NLP community.
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 introduces methods for distilling pretrained contextual representation to a static embeddings for the faster use. It proposes subword pooling and context combination, and its contribution is demonstrated with a suite of classic experiments for static word embeddings. My score for this paper is weakly rejected because (1) the motivation of the proposed approach is not clear. The paper title is about “distill”, but I can’t find out what the methods try to distill, what is the objective; (2) the goal of the paper is to obtain a static embedding performing like ELMO or BERT. As we know, the power of the contextual embedding is demonstrated by different NLP downstreaming task like MT, QA and a bunch of text classification tasks. In this paper, I don’t find those experiments; (3) this paper compared the results with word2vec and glove, but not clear which version to compare. And there are some re-embedding approaches that improve word2vec and glove such as “Word Re-Embedding via Manifold Dimensionality Retention”, the table in this re-embedding paper shows different version of glove and some of them are better than the proposed methods distilled from BERT.
assistant
This paper introduces methods for distilling pretrained contextual representation to a static embeddings for the faster use. It proposes subword pooling and context combination, and its contribution is demonstrated with a suite of classic experiments for static word embeddings. My score for this paper is weakly rejected because (1) the motivation of the proposed approach is not clear. The paper title is about “distill”, but I can’t find out what the methods try to distill, what is the objective; (2) the goal of the paper is to obtain a static embedding performing like ELMO or BERT. As we know, the power of the contextual embedding is demonstrated by different NLP downstreaming task like MT, QA and a bunch of text classification tasks. In this paper, I don’t find those experiments; (3) this paper compared the results with word2vec and glove, but not clear which version to compare. And there are some re-embedding approaches that improve word2vec and glove such as “Word Re-Embedding via Manifold Dimensionality Retention”, the table in this re-embedding paper shows different version of glove and some of them are better than the proposed methods distilled from BERT.