Unsupervised Word Discovery with Segmental Neural Language Models
We propose a segmental neural language model that combines the representational power of neural networks and the structure learning mechanism of Bayesian nonparametrics, and show that it learns to discover semantically meaningful units (e.g., morphemes and words) from unsegmented character sequences. The model generates text as a sequence of segments, where each segment is generated either character-by-character from a sequence model or as a single draw from a lexical memory that stores multi-character units. Its parameters are fit to maximize the marginal likelihood of the training data, summing over all segmentations of the input, and its hyperparameters are likewise set to optimize held-out marginal likelihood. To prevent the model from overusing the lexical memory, which leads to poor generalization and bad segmentation, we introduce a differentiable regularizer that penalizes based on the expected length of each segment. To our knowledge, this is the first demonstration of neural networks that have predictive distributions better than LSTM language models and also infer a segmentation into word-like units that are competitive with the best existing word discovery models.
 This paper presented a novel approach for modeling a sequence of characters as a sequence of latent segmentations. The challenge here was how to efficiently compute the marginal likelihood of a character sequence (exponential number different of segmentations). The author(s) overcame this by having a segment generation process independent from the previous segment (only depends on a sequence of characters). The inference is then required a forward algorithm. To generate a segment, a model can either select a lexical unit (pre-processed from a training corpus) or generate character by character. On the experiments, the author(s) showed that the model recovered semantical segmentation on many word segmentation dataset (including phonemes). The lexical memory and the length regularization both contribute significantly as shown in the analysis. The language modeling result (BPC) was also competitive with LSTM-based LMs. I think the overall model is interesting and well motivated, though it is a bit disappointing that the author(s) needed to use an extra regularizer to constraint the segment length (from the lexical memory?). Perhaps, the way they build a lexical memory should be investigated further. The experiment should also show an evidence that SNLM(+memory, -length) was overfitted as claimed. The validation and test dataset have been modified to remove "samples" containing OOV characters. How many have been removed? The author(s) could opt for an unknown character similar to many word-level datasets. The use of word segmentation data was quite clever, but this also downplayed other work that is not aimed to recover human-semantic segmentations. For example, a segment "doyou" on page 10 might be considered as a valid segmentation since it appears a whole lot. HM-LSTM though did poorly on the segmentation task but performed rather well on PTB LM task, but the author(s) decided to omit this comparison. Some minor comments: - A typo in the introduction "... semi-Markov model. The the characters inside ...". - Eq 3 is a bit hard to follow. Perhaps, a short derivation should be presented. - Is it possible to efficiently generate a sequence? [Updated after reconsidering other reviews] Although this paper misses some related work and comparison models, I think it still has a valid contribution to language modeling: a character-level language model that produces plausible word segmentation.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Unsupervised Word Discovery with Segmental Neural Language Models
We propose a segmental neural language model that combines the representational power of neural networks and the structure learning mechanism of Bayesian nonparametrics, and show that it learns to discover semantically meaningful units (e.g., morphemes and words) from unsegmented character sequences. The model generates text as a sequence of segments, where each segment is generated either character-by-character from a sequence model or as a single draw from a lexical memory that stores multi-character units. Its parameters are fit to maximize the marginal likelihood of the training data, summing over all segmentations of the input, and its hyperparameters are likewise set to optimize held-out marginal likelihood. To prevent the model from overusing the lexical memory, which leads to poor generalization and bad segmentation, we introduce a differentiable regularizer that penalizes based on the expected length of each segment. To our knowledge, this is the first demonstration of neural networks that have predictive distributions better than LSTM language models and also infer a segmentation into word-like units that are competitive with the best existing word discovery models.
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 presented a novel approach for modeling a sequence of characters as a sequence of latent segmentations. The challenge here was how to efficiently compute the marginal likelihood of a character sequence (exponential number different of segmentations). The author(s) overcame this by having a segment generation process independent from the previous segment (only depends on a sequence of characters). The inference is then required a forward algorithm. To generate a segment, a model can either select a lexical unit (pre-processed from a training corpus) or generate character by character. On the experiments, the author(s) showed that the model recovered semantical segmentation on many word segmentation dataset (including phonemes). The lexical memory and the length regularization both contribute significantly as shown in the analysis. The language modeling result (BPC) was also competitive with LSTM-based LMs. I think the overall model is interesting and well motivated, though it is a bit disappointing that the author(s) needed to use an extra regularizer to constraint the segment length (from the lexical memory?). Perhaps, the way they build a lexical memory should be investigated further. The experiment should also show an evidence that SNLM(+memory, -length) was overfitted as claimed. The validation and test dataset have been modified to remove "samples" containing OOV characters. How many have been removed? The author(s) could opt for an unknown character similar to many word-level datasets. The use of word segmentation data was quite clever, but this also downplayed other work that is not aimed to recover human-semantic segmentations. For example, a segment "doyou" on page 10 might be considered as a valid segmentation since it appears a whole lot. HM-LSTM though did poorly on the segmentation task but performed rather well on PTB LM task, but the author(s) decided to omit this comparison. Some minor comments: - A typo in the introduction "... semi-Markov model. The the characters inside ...". - Eq 3 is a bit hard to follow. Perhaps, a short derivation should be presented. - Is it possible to efficiently generate a sequence? [Updated after reconsidering other reviews] Although this paper misses some related work and comparison models, I think it still has a valid contribution to language modeling: a character-level language model that produces plausible word segmentation.
assistant
 This paper presented a novel approach for modeling a sequence of characters as a sequence of latent segmentations. The challenge here was how to efficiently compute the marginal likelihood of a character sequence (exponential number different of segmentations). The author(s) overcame this by having a segment generation process independent from the previous segment (only depends on a sequence of characters). The inference is then required a forward algorithm. To generate a segment, a model can either select a lexical unit (pre-processed from a training corpus) or generate character by character. On the experiments, the author(s) showed that the model recovered semantical segmentation on many word segmentation dataset (including phonemes). The lexical memory and the length regularization both contribute significantly as shown in the analysis. The language modeling result (BPC) was also competitive with LSTM-based LMs. I think the overall model is interesting and well motivated, though it is a bit disappointing that the author(s) needed to use an extra regularizer to constraint the segment length (from the lexical memory?). Perhaps, the way they build a lexical memory should be investigated further. The experiment should also show an evidence that SNLM(+memory, -length) was overfitted as claimed. The validation and test dataset have been modified to remove "samples" containing OOV characters. How many have been removed? The author(s) could opt for an unknown character similar to many word-level datasets. The use of word segmentation data was quite clever, but this also downplayed other work that is not aimed to recover human-semantic segmentations. For example, a segment "doyou" on page 10 might be considered as a valid segmentation since it appears a whole lot. HM-LSTM though did poorly on the segmentation task but performed rather well on PTB LM task, but the author(s) decided to omit this comparison. Some minor comments: - A typo in the introduction "... semi-Markov model. The the characters inside ...". - Eq 3 is a bit hard to follow. Perhaps, a short derivation should be presented. - Is it possible to efficiently generate a sequence? [Updated after reconsidering other reviews] Although this paper misses some related work and comparison models, I think it still has a valid contribution to language modeling: a character-level language model that produces plausible word segmentation.