What do you learn from context? Probing for sentence structure in contextualized word representations
Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.
 This is a nice paper that attempts to tease apart some questions about the effectiveness of contextual word embeddings (ELMo, CoVe, and the Transformer LM). The main question is about the value of context in these representations, and in particular how their ability to encode context allows them to also (implicitly) represent linguistic properties of words. What I really like about the paper is the “Edge probing” method it introduces. The idea is to probe the representations using diagnostic classifiers—something that’s already widespread practice—but to focus on the relationship between spans rather than individual words. This is really nice because it enables them to look at more than just tagging problems: the paper looks at syntactic constituency, dependencies, entity labels, and semantic role labeling. I think the combination of an interesting research question and a new method (which will probably be picked up by others working in this area) make this a strong candidate for ICLR. The paper is well-written and experimentally thorough. Nitpick: It would be nice to see some examples of cases where the edge probe is correct, and where it isn’t.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
What do you learn from context? Probing for sentence structure in contextualized word representations
Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.
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 is a nice paper that attempts to tease apart some questions about the effectiveness of contextual word embeddings (ELMo, CoVe, and the Transformer LM). The main question is about the value of context in these representations, and in particular how their ability to encode context allows them to also (implicitly) represent linguistic properties of words. What I really like about the paper is the “Edge probing” method it introduces. The idea is to probe the representations using diagnostic classifiers—something that’s already widespread practice—but to focus on the relationship between spans rather than individual words. This is really nice because it enables them to look at more than just tagging problems: the paper looks at syntactic constituency, dependencies, entity labels, and semantic role labeling. I think the combination of an interesting research question and a new method (which will probably be picked up by others working in this area) make this a strong candidate for ICLR. The paper is well-written and experimentally thorough. Nitpick: It would be nice to see some examples of cases where the edge probe is correct, and where it isn’t.
assistant
 This is a nice paper that attempts to tease apart some questions about the effectiveness of contextual word embeddings (ELMo, CoVe, and the Transformer LM). The main question is about the value of context in these representations, and in particular how their ability to encode context allows them to also (implicitly) represent linguistic properties of words. What I really like about the paper is the “Edge probing” method it introduces. The idea is to probe the representations using diagnostic classifiers—something that’s already widespread practice—but to focus on the relationship between spans rather than individual words. This is really nice because it enables them to look at more than just tagging problems: the paper looks at syntactic constituency, dependencies, entity labels, and semantic role labeling. I think the combination of an interesting research question and a new method (which will probably be picked up by others working in this area) make this a strong candidate for ICLR. The paper is well-written and experimentally thorough. Nitpick: It would be nice to see some examples of cases where the edge probe is correct, and where it isn’t.