Reference-Aware Language Models
We propose a general class of language models that treat reference as an explicit stochastic latent variable. This architecture allows models to create mentions of entities and their attributes by accessing external databases (required by, e.g., dialogue generation and recipe generation) and internal state (required by, e.g. language models which are aware of coreference). This facilitates the incorporation of information that can be accessed in predictable locations in databases or dis- course context, even when the targets of the reference may be rare words. Experiments on three tasks show our model variants outperform models based on deterministic attention.
This paper presents a new type of language model that treats entity references as latent variables. The paper is structured as three specialized models for three applications: dialog generation with references to database entries, recipe generation with references to ingredients, and text generation with coreference mentions. Despite some opaqueness in details that I will discuss later, the paper does a great job making the main idea coming through, which I think is quite interesting and definitely worth pursuing further. But it seems the paper was rushed into the deadline, as there are a few major weaknesses. The first major weakness is that the claimed latent variables are hardly latent in the actual empirical evaluation. As clarified by the authors via pre-review QAs, all mentions were assumed to be given to all model variants, and so, it would seem like an over-claim to call these variables as latent when they are in fact treated as observed variables. Is it because the models with latent variables were too difficult to train right? A related problem is the use of perplexity as an evaluation measure when comparing reference-aware language models to vanilla language models. Essentially the authors are comparing two language models defined over different event space, which is not a fair comparison. Because mentions were assumed to be given for the reference-aware language models, and because of the fact that mention generators are designed similar to a pointer network, the probability scores over mentions will naturally be higher, compared to the regular language model that needs to consider a much bigger vocabulary set. The effect is analogous to comparing language models with aggressive UNK (and a small vocabulary set) to a language models with no UNK (and a much larger vocabulary set). To mitigate this problem, the authors need to perform one of the following additional evaluations: either assuming no mention boundaries and marginalizing over all possibilities (treating latent variables as truly latent), or showing other types of evaluation beyond perplexity, for example, BLEU, METEOR, human evaluation etc on the corresponding generation task. The other major weakness is writing in terms of technical accuracy and completeness. I found many details opaque and confusing even after QAs. I wonder if the main challenge that hinders the quality of writing has something to do with having three very specialized models in one paper, each having a lot of details to be worked out, which may have not been extremely important for the main story of the paper, but nonetheless not negligible in order to understand what is going on with the paper. Perhaps the authors can restructure the paper so that the most important details are clearly worked out in the main body of the paper, especially in terms of latent variable handling — how to make mention detection and conference resolution truly latent, and if and when entity update helps, which in the current version is not elaborated at all, as it is mentioned only very briefly for the third application (coreference resolution) without any empirical comparisons to motivate the update operation.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Reference-Aware Language Models
We propose a general class of language models that treat reference as an explicit stochastic latent variable. This architecture allows models to create mentions of entities and their attributes by accessing external databases (required by, e.g., dialogue generation and recipe generation) and internal state (required by, e.g. language models which are aware of coreference). This facilitates the incorporation of information that can be accessed in predictable locations in databases or dis- course context, even when the targets of the reference may be rare words. Experiments on three tasks show our model variants outperform models based on deterministic attention.
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 presents a new type of language model that treats entity references as latent variables. The paper is structured as three specialized models for three applications: dialog generation with references to database entries, recipe generation with references to ingredients, and text generation with coreference mentions. Despite some opaqueness in details that I will discuss later, the paper does a great job making the main idea coming through, which I think is quite interesting and definitely worth pursuing further. But it seems the paper was rushed into the deadline, as there are a few major weaknesses. The first major weakness is that the claimed latent variables are hardly latent in the actual empirical evaluation. As clarified by the authors via pre-review QAs, all mentions were assumed to be given to all model variants, and so, it would seem like an over-claim to call these variables as latent when they are in fact treated as observed variables. Is it because the models with latent variables were too difficult to train right? A related problem is the use of perplexity as an evaluation measure when comparing reference-aware language models to vanilla language models. Essentially the authors are comparing two language models defined over different event space, which is not a fair comparison. Because mentions were assumed to be given for the reference-aware language models, and because of the fact that mention generators are designed similar to a pointer network, the probability scores over mentions will naturally be higher, compared to the regular language model that needs to consider a much bigger vocabulary set. The effect is analogous to comparing language models with aggressive UNK (and a small vocabulary set) to a language models with no UNK (and a much larger vocabulary set). To mitigate this problem, the authors need to perform one of the following additional evaluations: either assuming no mention boundaries and marginalizing over all possibilities (treating latent variables as truly latent), or showing other types of evaluation beyond perplexity, for example, BLEU, METEOR, human evaluation etc on the corresponding generation task. The other major weakness is writing in terms of technical accuracy and completeness. I found many details opaque and confusing even after QAs. I wonder if the main challenge that hinders the quality of writing has something to do with having three very specialized models in one paper, each having a lot of details to be worked out, which may have not been extremely important for the main story of the paper, but nonetheless not negligible in order to understand what is going on with the paper. Perhaps the authors can restructure the paper so that the most important details are clearly worked out in the main body of the paper, especially in terms of latent variable handling — how to make mention detection and conference resolution truly latent, and if and when entity update helps, which in the current version is not elaborated at all, as it is mentioned only very briefly for the third application (coreference resolution) without any empirical comparisons to motivate the update operation.
assistant
This paper presents a new type of language model that treats entity references as latent variables. The paper is structured as three specialized models for three applications: dialog generation with references to database entries, recipe generation with references to ingredients, and text generation with coreference mentions. Despite some opaqueness in details that I will discuss later, the paper does a great job making the main idea coming through, which I think is quite interesting and definitely worth pursuing further. But it seems the paper was rushed into the deadline, as there are a few major weaknesses. The first major weakness is that the claimed latent variables are hardly latent in the actual empirical evaluation. As clarified by the authors via pre-review QAs, all mentions were assumed to be given to all model variants, and so, it would seem like an over-claim to call these variables as latent when they are in fact treated as observed variables. Is it because the models with latent variables were too difficult to train right? A related problem is the use of perplexity as an evaluation measure when comparing reference-aware language models to vanilla language models. Essentially the authors are comparing two language models defined over different event space, which is not a fair comparison. Because mentions were assumed to be given for the reference-aware language models, and because of the fact that mention generators are designed similar to a pointer network, the probability scores over mentions will naturally be higher, compared to the regular language model that needs to consider a much bigger vocabulary set. The effect is analogous to comparing language models with aggressive UNK (and a small vocabulary set) to a language models with no UNK (and a much larger vocabulary set). To mitigate this problem, the authors need to perform one of the following additional evaluations: either assuming no mention boundaries and marginalizing over all possibilities (treating latent variables as truly latent), or showing other types of evaluation beyond perplexity, for example, BLEU, METEOR, human evaluation etc on the corresponding generation task. The other major weakness is writing in terms of technical accuracy and completeness. I found many details opaque and confusing even after QAs. I wonder if the main challenge that hinders the quality of writing has something to do with having three very specialized models in one paper, each having a lot of details to be worked out, which may have not been extremely important for the main story of the paper, but nonetheless not negligible in order to understand what is going on with the paper. Perhaps the authors can restructure the paper so that the most important details are clearly worked out in the main body of the paper, especially in terms of latent variable handling — how to make mention detection and conference resolution truly latent, and if and when entity update helps, which in the current version is not elaborated at all, as it is mentioned only very briefly for the third application (coreference resolution) without any empirical comparisons to motivate the update operation.