Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
The use of deep pre-trained transformers has led to remarkable progress in a number of applications (Devlin et al., 2018). For tasks that make pairwise comparisons between sequences, matching a given input with a corresponding label, two approaches are common: Cross-encoders performing full self-attention over the pair and Bi-encoders encoding the pair separately. The former often performs better, but is too slow for practical use. In this work, we develop a new transformer architecture, the Poly-encoder, that learns global rather than token level self-attention features. We perform a detailed comparison of all three approaches, including what pre-training and fine-tuning strategies work best. We show our models achieve state-of-the-art results on four tasks; that Poly-encoders are faster than Cross-encoders and more accurate than Bi-encoders; and that the best results are obtained by pre-training on large datasets similar to the downstream tasks.
This paper describes an approach for scoring sentences based on pre-trained transformers. The paper describes two main existing approaches for this task, namely bi-encoders and cross-encoders and then proposes a new formulation called poly-encoders which aims to sit between the existing approaches offering high accuracy -- similarly to cross-encoders -- and high efficiency -- similarly to bi-encoders. The paper is well written and although this is not related to my research I enjoying reading it. The approach proposed seems reasonable to me, and of sufficient novelty while the results presented are impressive. Moreover the paper seems a good fit for ICLR.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
The use of deep pre-trained transformers has led to remarkable progress in a number of applications (Devlin et al., 2018). For tasks that make pairwise comparisons between sequences, matching a given input with a corresponding label, two approaches are common: Cross-encoders performing full self-attention over the pair and Bi-encoders encoding the pair separately. The former often performs better, but is too slow for practical use. In this work, we develop a new transformer architecture, the Poly-encoder, that learns global rather than token level self-attention features. We perform a detailed comparison of all three approaches, including what pre-training and fine-tuning strategies work best. We show our models achieve state-of-the-art results on four tasks; that Poly-encoders are faster than Cross-encoders and more accurate than Bi-encoders; and that the best results are obtained by pre-training on large datasets similar to the downstream tasks.
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 describes an approach for scoring sentences based on pre-trained transformers. The paper describes two main existing approaches for this task, namely bi-encoders and cross-encoders and then proposes a new formulation called poly-encoders which aims to sit between the existing approaches offering high accuracy -- similarly to cross-encoders -- and high efficiency -- similarly to bi-encoders. The paper is well written and although this is not related to my research I enjoying reading it. The approach proposed seems reasonable to me, and of sufficient novelty while the results presented are impressive. Moreover the paper seems a good fit for ICLR.
assistant
This paper describes an approach for scoring sentences based on pre-trained transformers. The paper describes two main existing approaches for this task, namely bi-encoders and cross-encoders and then proposes a new formulation called poly-encoders which aims to sit between the existing approaches offering high accuracy -- similarly to cross-encoders -- and high efficiency -- similarly to bi-encoders. The paper is well written and although this is not related to my research I enjoying reading it. The approach proposed seems reasonable to me, and of sufficient novelty while the results presented are impressive. Moreover the paper seems a good fit for ICLR.