Learning to Compute Word Embeddings On the Fly
Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the ``long tail** of this distribution requires enormous amounts of data. Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-train embeddings on external data, or treat all rare words as out-of-vocabulary words with a unique representation. We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task. We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.
 This paper illustrates a method to compute produce word embeddings on the fly for rare words, using a pragmatic combination of existing ideas: * Backing off to a separate decoder for rare words a la Luong and Manning (https://arxiv.org/pdf/1604.00788.pdf, should be cited, though the idea might be older). * Using character-level models a la Ling et al. * Using dictionary embeddings a la Hill et al. None of these ideas are new before but I haven’t seen them combined in this way before. This is a very practical idea, well-explained with a thorough set of experiments across three different tasks. The paper is not surprising but this seems like an effective technique for people who want to build effective systems with whatever data they’ve got.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning to Compute Word Embeddings On the Fly
Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the ``long tail** of this distribution requires enormous amounts of data. Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-train embeddings on external data, or treat all rare words as out-of-vocabulary words with a unique representation. We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task. We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.
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 illustrates a method to compute produce word embeddings on the fly for rare words, using a pragmatic combination of existing ideas: * Backing off to a separate decoder for rare words a la Luong and Manning (https://arxiv.org/pdf/1604.00788.pdf, should be cited, though the idea might be older). * Using character-level models a la Ling et al. * Using dictionary embeddings a la Hill et al. None of these ideas are new before but I haven’t seen them combined in this way before. This is a very practical idea, well-explained with a thorough set of experiments across three different tasks. The paper is not surprising but this seems like an effective technique for people who want to build effective systems with whatever data they’ve got.
assistant
 This paper illustrates a method to compute produce word embeddings on the fly for rare words, using a pragmatic combination of existing ideas: * Backing off to a separate decoder for rare words a la Luong and Manning (https://arxiv.org/pdf/1604.00788.pdf, should be cited, though the idea might be older). * Using character-level models a la Ling et al. * Using dictionary embeddings a la Hill et al. None of these ideas are new before but I haven’t seen them combined in this way before. This is a very practical idea, well-explained with a thorough set of experiments across three different tasks. The paper is not surprising but this seems like an effective technique for people who want to build effective systems with whatever data they’ve got.