Learning to Express in Knowledge-Grounded Conversation
Authors: Xueliang Zhao
Tingchen Fu
Chongyang Tao
Wei Wu
Dongyan Zhao
Rui Yan
Venue: NeurIPS 2021 Submitted (2021)

Abstract: Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses. Existing studies focus on how to synthesize a response with proper knowledge, yet neglect that the same knowledge could be expressed differently by speakers even under the same context. In this work, we mainly consider two aspects of knowledge expression, namely the structure of the response and style of the content in each part. We therefore introduce two sequential latent variables to represent the structure and the content style respectively. We propose a segmentation-based generation model and optimize the model by a variational approach to discover the underlying pattern of knowledge expression in a response. Evaluation results on two benchmarks indicate that our model can learn the structure style defined by a few examples and generate responses in desired content style.


Learning to Express in Knowledge-Grounde Conversation
Anonymous Author(s)
Affiliation
Address
ema i
Abstract
Grounding dialogue generation by extra knowledge has shown great potentials towards building system capable of replying with knowledgeable and engaging responses. Existing studies focus on how to synthesize a response with proper knowledge, yet neglect that the same knowledge could be expressed differently by speakers even under the same context. aspects of knowledge expression, namely the structure of the response and style of the content in each part. We therefore introduce two sequential latent variables represent the structure and the content style respectively. We propose a segmentation-bast generation model and optimize the model by a variational approach discover the underlying pattern of knowledge expression in response. Evaluation results on two benchmarks indicate that our model can learn the structure style defined by few examples and generate responses in desired content style.
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this work, we mainly consider
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Introduction
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14 Human-machin conversation long-standing goal of artificial intelligence (AI). In the past few years, with advances in deep learning (28 5 31 and availability of huge amount of human conversations on social media (. building an open domain dialogue system with data-driven approaches has attracted increasing attention from the community of Al and NLP. By synthesizing response with text generation techniques (32), current natural models are able to naturally reply to user prompts. Despite the impressive progress, existing generation models are notorious for replying with generic and bland responses, resulting in meaningless and boring conversations [13]. Such deficiency is particularly severe when human participants attempt to dive into specific topics in conversation (3].
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To bridge the gap, some researchers resort to ground dialogue generation by extra knowledge such as unstructured documents 49 By this means, the documents (e.g., wiki articles) serve as content
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sources and make a dialogue system knowledgeable regarding various concepts in discussion.
26 However, existing studies focus how synthesize response with proper knowledge [T] 45).
but pay little attention to the fact that the same knowledge could be expressed differently even under
28 the same context. These models usually employ a regular decoder to generate response in an ruto-regressive manner given the contextual representations of knowledge and dialogue context, 30 which makes the generation process less explainable and controllable.
31 In general, we break down the expression style of a response into two components: the structure of
32 the response and the style of the content in each part. First, the knowledge expression in response
33 varies in structure, including but not limited to the position and the length of knowledge expression.
34 As the example shown in Table T knowledge-related phrases and clauses could be long. like "And I'd
35 give credit to three different voice actors for anna.", or short, like "74 in Metacritics" Besides, they
36 may appear at the beginning of the sentence, or at the end. For the sake of description, we decompose response into a sequence of non-overlapping segments, each is either related to certain background
Submitted to 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Do not distribute.

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Table 1: A case from CMU DoG. Given the same knowledge and context, the last two turns in left and right conversations exhibit positive and negative sentiments, respectively. Each utterance can be decomposed into knowledge-related and Anowledge-irrlevant segments.
Knowledge
MovieName: Frozen
Year:
Kating: Rottes Tomatoes: Bolt Metacritics: 74/100, Claemaaooee:
Geaze: Comedy, Adventure, Animation
Dirrector: Chris Rack, Jennifer Las
Cist: Kristen Bell na Anne, the 18 year--old Princess of Arendelle and Elsa's younger sister, Livey Stubenrauch Anna,
Katic Lopez 5-year old Anna (sloging), Agatha Lee Monn 9-year-old old Anna...
Converaatios
User: really surprised disnnyy chose Kristen
reaily surprised disney Kristen Bell to
the voice of Amna Frozem
the voice Anna in Frowen
Userz: Yes. I didn't imagine it be her!
Userz: Yes, didd't imagine it' be her!
Userz What du you think about the rating?
Usera: What do you think about the rating?
Usern: 74 Metacrifios, I bellive it deserves, indeed.
User: The rating is 24 Metacritios Let high enough for
User: And I'd give crecitt to three different voice actors for anna.
Disney
I'm really impressed. What about your?
And do think overkill to different voice
actors for anna. Do you agnee??
38 knowledge and diverse in content style, or almost irrelevant the knowledge but simply playing the
39 role of stitching the context and carrying on the conversation. We therefore define the structure style
40 as the distribution and number of two kinds of segments. Structure style itself is far from dominant in the sentence expression, since different speakers could convey converse attitude even the context and the knowledge are exactly the same, as shown in Table [ So it is necessary to introduce the content style as the expression fashion within each knowledge-relater segment. We further introduce two latent variables to facilitate end-to end-to-end training, one for predicting the start and end positions of segment, the other for deciding the category of each segment. Since the human annotations for sentence segmentation are absent and enumerating over all possibilities to maximize the likelihood of the response is time-consuming, we propose variational framework for gegmentation-based generation and induce an evidence lower bound of the likelihood.
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Formally, our model is on the basis of incoder-decoeer architecture. The encoder is to obtain the contextual representation of conversational context and knowledge in a regular way. The decoder consists of three types of modules: (I) a context module, for response only based on context without knowledge; (2) a
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module, for response referring knowledges but without particular style: and (3) one or more stylized-knowledy module, for response referring knowledges and with a specific style. The context module is the only module not relying on knowledge, but simply paying attention to contextual information. Compared with plain-knowledge module, module has unique adapters, which is their primary discrepancy. When decoding, the decoder first predicts the segmentation of the response and then makes choice in three kinds of modules to generate a single segment. Both the segmentation and the module selection are instructed under sequential latent variables.
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We train our model on the Reddit Corpus published by 15 and evaluate our model on two bench- marks of knowledge- prowledge-rouunded conversation: Wizard of Wikipedia (Wzzrdd 3 and CMU Document Grounded CCM DoG) [49]. Evaluation results indicate that our model can significantly outperform state-of-the-arr methods in the zero-resource setting (i.e., only trained on the Reddit Corpus). addition, the performance of our model improves significantly on Wizard and CMU_ with the presence of only 10% training data and the segment distributions after fine-tuning are consis- tent with our prior knowledge about the two datasets, indicating that our model can learn the structure style with little cost. Finally, model outperforms previous sfate-of-the-ar models on the accuracy of performing sentiment classification using generated responses. It is worth noting that our model achieves 10%+ accuracy improvement on Wizard Seen, 12%+ accuracy improvement on Wizard Unseen, and 12%+ accuracy improvement on CMU CMU_DoG B_o than the present ofate-of-the-art model, which indicates that the model can be controlled to express knowledge with the desired content style.
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72 Contributions this work are three-fold: (1) exploration the knowledge expression knowledge- grounded conversation; (2) proposal of a variational segmentation-basel generation model to discover 74 the underlying expression style a response; (3) empirical verification of the effectiveness of the proposed model on two benchmarks of knowledge- -egolldddegg conversation.
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Boundory Indicator
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m7g
bos
R5
Module Indicator
Depy
Capy
Updare
Coppy
Copy
Updone
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context module
plain knowledge module
stylized-knowledge
module
Dialogue
Knowledge
Figure 1: Architecture of the proposed model.
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Related Work
77 Early research on end-to-end open- open-domain dialogue generation is inspired by the successful applica- tion of neural networks machine translation (0 24 [3). On the vanilla encoder-decoder architec-
79 ture, various extensions have been made to model the structure of dialogue contexts [3 37 39 to
improve diversity of responses [13 36 43 29];
control attributes of responses [38 46 40) 34 21
81 and to bias responses to some specific personas 14 41 Recently, grounding dialogue generation
82 by extra knowledge has seemed promising bridge the gap between conversation with existing systems and conversation with humans, and the knowledge could be obtained from knowledge graphs retrieved from unstructured documents 3 16
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45 [5] or extracted from visual 26 9. In this work, we study document- -eround dialogue generation. Rather than selecting knowledge relevant to dialogue context and directly exploiting pre- pre-trained language models to generate the response, we focus on expressing knowledge in this task.
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back ground 19
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The idea of sequence modeling via segmentation natural language processing tasks. In the field of text segmentation, 33 propose a probabilistic model for sequence modeling via their segmentation and "Sleep-WAr Network" (SWAN) method.
33 has attracted widespread attention in several
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91 In machine translation, [ propose a neural phrase-based machine translation system that models phrase structures the target language using SWAN. In data-to-text generation. [35 develop neural, template-like generation model based on an HSMM decoder, which can be learned tractably by backpropagating through a dynamic program; to tackle the problem of weak Markov assumption for the segment transition probability, (25 propose to explicitly segment target text into fragments and align them with their data correspondences, and jointly learn the segmentation and correspondence via dynamic programming. Though quite few methods have been proposed to reduce the computational complexity 33 25 using dynamic programming to calculate likelihood still expensive. This work introduces two sequential latent variables to model the knowledge expression and proposes variational segmentation-bant generation framework, which enjoys less computation cost.
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Approach
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3.1
Problem Formalization and Motivation
Suppose that we have dataset D {UU,, K,, R,) where Ví E {1,..., N). K, serves as background knowledge the dialogue (U,, R,) with K,, the j-th sentence, U, is the context of the dialogue with U,, the j-th utterance, and R; is the response. To bias the expression to specific structure style, we further assume that there are few examples Daty ((U,, K,, R.) M.M provided by
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users depicting the required style for knowledge expression. Note that we have N M, since corpus in specific expression style is rare and difficult to acquire. The goal is to learn generation model
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109 Pe R, K) (0 denotes the parameters of the model) from D, to generate response R following
110 Po K) K) given a new dialogue context U and the associated knowledge K. Besides, one can either (1) bias the structure style of Po R K) to Dsty with little cost; or (2) switch the content
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style of knowledge expression R.
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As mentioned above, the response can be decomposed into a sequence of segments, each is other knowledge-relatee various in expression, or knowledge-irrelvant, Therefore manipulating the expression style of response could be split into two subproblems. One is to control the structure style, in other word, distribution and number of two kinds of segments. The other subproblem the content style, or generating every knowledge-relann reldded segment in desired content style, such as positive style or negative style or other customized styles defined by users. To solve the two subproblems. generation model, which could automatically detect and predict the segmental structure of the response and then generate segments of response one by one. Each or knowledge-related, and knowledge-related segments could be expressed in arbitrary style, defined and manipulated by users. Both the segmentation and the choice are modeled by latent variable, so as to facilitate end-to-end training. Furthermore, to guarantee the efficiency and practicality of our model, we propose a variational approach to optimize the evidence lower bound (ELBO) of the likelihood of response to circumvent directly marginalizing over all possible combinations of segmentation and action choice, which is time-consuming in both training and test stages.
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we propose a segmentation-basee
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segment is either knowledge-irrelevan
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3.2
Model Architecture
Figure gives an overview of the proposed model, which is based on the decoder architecture. The encoder generates the contextual representations of the dialogue and knowledge. while the decoder generates the segments one after another. hy encodes the dialogue context up to timestep with N denoting the number of decoder layers. Given R = (r1, '", lt, "",, rt,)) with ra referring the 1-th token of R whose length supposed be I,, the variable = {=+)/= is utilized to control the choice of module of each segment(Module Indicator), and its historical information is encoded
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by (c.)/ M {mmm )/= is sequence of binary variables and used to determine the boundary of each segment(Boundary Indicator). Specifically, m, = 1 indicates that the current segment is already completed and a new segment should be created at the next timestep. Otherwise m, 0 and the current segment remains unfinished. The generative process is disassembled into two steps: (1) determine the type of a new segment based previously generated text and previous segment types; (2) generate within the current segment until the binary variable m, = 1.
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Context and Knowledge Encoding. We exploit B2 as the backbone of our architecture, which is pre-trained using variety of denoising objectives and achieves state-of-the-art results we simply concatenate
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range of text generation tasks. Given the dialogue context U
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them as (u1,"., ut,)). ). Similarly, we concatenate the associated knowledge K (K1,"', Km) as
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"", kig ). La and 4a are the length of dialogue context and background knowledge respectively.
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The input of the encoder is then defined as:
I = [BOS]K (ko ... AI, [EOS]u....... ... ur_ [EOS].
(1)
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The input then passes through the stacked self-attention layers and results in a knowledge-aware context representation C, and context-aware knowledge representation K. Specifically, the context- aware knowledge representation defined as K [h .... h where h. is the last layer
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of BART encoder at time f. Similarly, the knowledge-awee aware context representation is defined as hone 22.
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C- [hipe
Prior of Module Indicator. We use the sequential discrete latent variable Z {2 to decide which module to invoke at each timestep. The transition of 2, occurs only when segment is completed, which is decided by the binary boundary variable M. The prior quantifies the distribution of before we observe the segment, and it is reasonable to assume that the prior of depends on previous module choices Zet and previously generated text. As a result, the transition of Z is implemented follows:
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po. (Zdl 2,e, m ((zelce) + (1 m:-1) -6 2--1):
(2)

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where o2 encodes all previous latent state Zet and generated text Fet as follows:
M Sz-run (.- (1 m4-1) Ct-1-
(3)
2 [[e-1;; h with ez-1 the embedding of ž and 1 the representation of last generated token. Specifically, M4-1 means that the next timestep is still in the same segment as the previous timestep /. - and thus the latent variable 2 should not be updated. Otherwise, it means that current segment completed and 2 updated with the transition function (() Because we only have Naty + 2 options when choosing module, where Naty is the number of different user-defined styles, in addition with default styles, so in this model, the latent variable 2, ranges in natural integer to denote corresponding style type. Specifically, 2 0 denotes choosing the context expression module to generate snowledge-irreleann segment; 21 tells the model to choose the knowledge expression module without specially customized style; we leave the 2, 2 2 to be user-defined so as to select the knowledge expression module combined with customized style. The transition function c.) is then implemented as a multinomial distribution parameterized by Softmax(/2-mip
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7( 2
Prior of Boundary Indicator. The boundary indicator M = (m²) 1 depicts the segmental struc- ture of the response, with m, = indicates that a new segment will start at time ( Presumably, the prior of m, could be inferred from Tgr and 24. We model the distribution pe... (milrss, 2x) by
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Bernoulli distribution parameterized by o (i Neee
=e1)t-1))). where a denotes the sigmoid function and fm-mip is multi-layer perceptron network.
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Stylized Generation As mentioned above, the generation process involves scheduling different modules according to 2t- Here we give a systematic description of the generation process. The decoder accepts the token generated last timestep layers, finally obtains dense representation.
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T--1 rt-1 as input, performs transformation in N decoder
We use h² to denote the hidden state after the I-th layer at timestep t, which is shorthand for h hidec for brevity. Specially, hς is the output of the embedding layer. When 2, = 0, it implies that knowledge encoding is unnecessary for current segment so hy is defined as:
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h DecoderLayerr(h!- (),,
(4)
where H [h],,",,hi, h[_1 is sequence of decoder hidden states in previous timestep, and C is the context representation mentioned above. The implementation of DecoderLayer( is identical to the vanilla Transformer [31] where h first plays self-attention on H then performs C. The probability p(relree; 2 0) is defined as multinomial distribution parameterized by Coftmax(fr-mlp (h," )). where h2 encodes the generated tokens up to timestep
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cross-attention
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t I. When 2 = 1, the implementation of decoder layer is analogous to the 2; = 0 case except that
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we replace C with K, since knowledge is needed:
h (e ,,.,,K) K).
(5)
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To generate a segment with particular customized style when 2, 2 2, we introduce some adapters to bias the generation. Specifically, the hidden state hĄ is defined as:
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h DecoderLayer, (h! H K),
(6)
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where DecoderLayer,.ds () (s.s)) that we need to introduce a separate set of adapters for each style. To make the style fine-grained and adjustable, each style has a unique set of adapters. Different styles have no adapter common. In addition, our model has the ability learn to express in any type of style, as long as discriminator for the desired style is provided.
denotes the transformer decoder layer with adapters inserted. Note
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3.3 Learning Details
We introduce auxiliary distributions 2o... (M)R) I'L Io... Rm.)R) and 4o. (Z)M, R) I Go. (zo|A, R),, which serves as an approximation to the intractable posterior of the bound-
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We then apply variational approximation which gives 200 the following evidence lower bound objective 6]
199 indicator M and the module indicator Z.
po (R]U, K) 2 Byo. (M/R) Bgo. [yo. RZ) Σ
log Logpo(ralrie,,
Σ m (so R) po. (z)))
Σ t-1
DKL (acl(ge (0mm (milR)lpos po... (mi)):
(7)
where pg, (z+) and pe., (m²) stand for pe, (Ealrst; Eet, m)-1) and pe... (malr 2t) respectively, and (-l-) refers to Kullback-Leibler divergence. Detailed derivations are presented in supplementary material.
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Base on the intuition that the response provides hints about the segmentation, we construct the posterior distribution 1ém. (m,)Ft) as a Bernoulli distribution parameterized by a (i s/2 is feature extracted from bi-directional LSTM (R).. Since the module indicator keeps unchanged within a segment, the posterior distribution qó, M, R) is conditioned on the boundary indicator m4-1 and defined as:
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96. (2) m (2(1b))) (1- m(-1) (0-1)--(45)) -02)=((2)))) 2--1,,
(8)
where the transition function ((2) (1)) (()))))) is implemented as multinomial distribution parameterized by Softmax (f'-m (V))). Once we have the posterior distribution, we apply Gumbel-Softmall [10] with
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straight-through estimators (2 to take samples of m, and 2t.
Weak Supervision on M and Z. We first StanfordNLP toolkit 17 the training set as sequence of segments, and use M {m<)/= denote the results of segmentation labeling. The pseudo label of module choice Ž )- is tagged in similar way to multiclass classification, determined by (1) the similarity between each segment and knowledge and (2) the classification confidence of the style discriminator. More details about the construction of and M are provided in the supplementary material.
parse every response in
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With Z and M, the loss function of weak supervision is defined as:
Lm log Po.. (millsse Ee);
(9)
C2 m logpo, (Zelrets (a,
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The learning algorithm is summarized the supplementary material.
Experiments
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4.1
Datasets
We test our model on benchmarks of knowledge-grounded dialogue generation, including Wizard of Wikipedia (Wizard) and CMU Document Grounded Conversations (CMU DoG) 44 Both datasets are split into training sets, validation sets, and test sets by the data owners. Topics in Wizard cover wide range (1, (1,365 in total), and each conversation happens between wizard who has access the knowledge about a specific topic and an apprentice who is just eager to learn from the wizard about
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the topic. The test set is split into two subsets: Test Seen and Test Unseen. Test Seen only contains dialogues with topics that have already appeared in the training set, while topics in Test Unseen never appear in the training set and the validation set. We follow [3] and conduct the pre-processing with the code published on ParlA Different from Wizard, CMU DoG focuses movie domain, and
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besides wizard-an aaprrnnice conversations, the data also contain conversations between two workers who know the document and try to discuss the content in depth. In both datasets, only the turns where knowledge is accessible are considered in response generation. More details are described in the supplementary material.
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We choose the Reddit Corpus published by [15 as D. The data contains 842, 521 context-knowledge response triples for training and 2, 737 wwweeee--nnowllee-espoooooo
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triples for validation. On average, each dialogue contains 3.1 utterances in both sets, and the average length of the utterance is 16.0 in training and is 16.1 in validation. The dataset enjoys a great diversity of expression styles thanks to the large scale of corpus and little restriction on expression. We use part of the training data of Wizard
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and CMU DoG as D cty respectively, for these two datasets are distinctive in expression style and differ from each other. The dialogues in CMU DoG tend to be causal and short, with most utterances
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irrelevant to knowledge. While the responses in Wizard are usually long and knowledgeable, as some
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phrases are directly extracted from wiki articles.
4.2
Experimental Setup
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In this paper, we mainly consider two experimental setups, corresponding to the two subproblems mentioned in SeeB.I To explore how our model can be used control the distribution of different kinds of segments (Knowledge-related and knowledge-ireeleannt) we first train the model on the Reddit Corpus and then fine-tune it on a small amount of examples in Wizard and CMU_Dop respectively. To verify whether our model can generate the knowledge-related segments in the desired style, we still train the model on the Reddit Corpus, and use a style tag to control the generation 250
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process. In this experimental setup, we are primarily concerned with generating with two kinds of styles, positive and negative, where 2, = 2- min(1, [1, 2x) tells the model to generate a response in positive sentiment and = 3 10mmmmnn1111 24) is for response in negative sentiment.
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Evaluation Metrics. We choose distinct and unigram Fl 3 as metrics, where the Fl metric is cal- culated with the code published at //attts://githuee ctttuuu.cooo/ooooooo Facebookresserchh b wllo//astee//peeee http:///ppa.aaa//eeeeeeeteteteee
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/ -yy Distinet-1 (D-1) and Distinct-2 (D-2) are cal- culated as ratios of distinct unigrams and bigrams in responses, respectively. We also employ classification accuracy as the evaluation metries for style control experiments. Specifically, we exploit Roberta trained on the SST-2 training set [27] as the evaluator, which is more accurate than that from the classifiers in [66].
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For the exploration of the first subproblem, select the following models as baselines: (I) B2RT[22: a model that achieves state-of-the-art performance on various text generation tasks. Note that our model degrades into BART once we remove the module indicator Z and the boundary indicator M; (2) Zero-resouree Knowledge-grounded Conversation (ZRKGC) [3]: a model
Baselines.
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that is based on UniLM 4 and optimized with Generalized EM method. The model is trained on the Reddit Corpus and achieves comparable performance with of-te-of-the-art methods that rely on Nhowledgeggouundee dialogues for training. For the second subproblem, following models as baselines: (1) (1)Emotional Chatting Machine (ECM) 47 : a model which can
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consider the
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generate appropriate responses not only content-relevan but also emotional consistent; (2)variant
of Di41oGPT[42): DialoGPT is a model that is pre-trained on large-scale conversation corpus and attains a performance close to human in single-turn dialogues. As DialoGPT is not designed for sentiment control, we add a sentiment indicating token at the first of the sequence and explore
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whether such simple heuristics works for controlling knowledge expression. Comparisons with more of art models are provided in the supplementary material.
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4.3
Results on Learning Structure Style
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In this section, we demonstrate the effectiveness of our segmentation-based generation framework in both low-resource setting and zero-resource setting and empirically verify that our model can learn structure style with few annotated examples. In zero-resource setting, we trained our model on the Reddit Corpus published by [15] and tested on Wizard and CMU DoG respectively. Automatic It could be observed that: (1) our model significantly
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evaluation results are shown in Table 2
httpa: htrpat//cithub.com pwwcan/ SRKGC
attps://github. coni/ecn.
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297 F Fine-tune with less annotated data. We first train the model on the Reddit Corpus and then fine- tune it with the amount of annotated data(e.g.. Wizard and CMU BoGG gradually increasing from 2% to 10%. To have a more intuitive understanding of the effects of latent variables Z. and M, we Table 2: Automatic evaluation results. Numbers in bold mean that the improvement to the best
performing baseline is statistically significant (t-test with p-value < 0.05).
Wizard Seen
Wizard Unseen
CMU DoG
Training Data
Models
F1
D-1
1-2
F1
D-1
13-2
F1
1>-1
D-2
BART
18.4
0.07
0.355
18.4
0.049
0.237
9.8
0.021
0.131
Roddit Corpus
ZRKGC
18.9
0.055
0.246
18.8
0.037
0.179
12.2
0.015
0.094
Our Model
19.3
0,082
0.383
19.2
0.060
0.292
12.2
0.028
0.186
BART
18.9
0.073
0.357
18.8
0.049
0.235
10.1
0.019
0.110
Reddit Corpus
ZRKGC
19.1
0.072
0.309
18.9
0.048
0.209
13.7
0.010
0.062
10% annotated data
Our Model
20.4
0.073
0.366
20.0
0.052
0.270
14.4
0.015
0.122
20.5
cur modal
BART
20.5
BART
nocel
15
BART
model
20.0
20.0
15
19.5
19.0
19.0
11
18.6
18.5
10
10%
18.0
2%
4%
8%
10%
0%
2%
4%
6%
8%
10%
Training Data
Training Data
Traninn Data
(a) Wizard Seen
(b) Wizard Unseen
(c) CMU DoG
Figure 2: Performance of different models wrt. training data size.
outperforms ZRKGC and BART most metrics and achieves the new state-of-the the-tf------rt--- art performance on Wizard. is impressive that our model exceeds BART in CMU_Dot especially since the proposed model degrades into BART without two sequential latent variables Z and M. The result serves as strong evidence for the effect of two latent variables, which enable the model to learn complex expression style in Reddit Corpus to handle flexible expression in CMU DoG. By contrast, BART is far from satisfying with only regular decoder. (2) our model exceeds ZRKGC significantly in terms of Distinct metrics, for ZRKGC mainly focuses on leverage external knowledge sources for response generation, but falls short on expression diversity. In low-resource setting, after training our model on the Reddit Corpus, we then fine-tune it with only 10% training size of Wizard and CMU_DoG respectively (i.e., Daty in 3.11 to adjust p(z:) and p(m:) to a new structure style. When provided with only 10% training data, our model gets obvious improvement (~ 1% increase in FI) in contrast with BART (~ 0.5% increase in FI) and ZRKGC (~ 0.2% increase in FI), proving that the proposed model can learn more sophisticated structure style through quickly adjustment on a specific dataset with little cost. Furthermore, we are interested in its potential in learning with less annotated data. We also want to investigate how our model is adjusted to different annotated data. Exploration of these two topics is as follows.
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compare the proposed model with BART, which generates the response with single decoder. The evaluation results are shown in Figure 2 It can be concluded from the result that: (1) our model can learn the expression style of particular dataset more efficiently. As the training data increase, our model has a more significant improvement in terms of the Fl metric; (2) our model performs better in meager resources since there a considerable gap between our model and BART when the training data is close to 0%; (3) the expression style of CMU_ __D can be learned with less data because the model has a significant change in performance after using 2% CMU BoG training data.
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Refashioning of knowledge-related segments. To know how our model adjusts to differentt datasets, we compare the knowledge-related segments before and after trained with an- notated data from two aspects: (1) the average proportion of knowledge-relan related segments (pkig) in a sentence; (2) the aver-
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age proportion of words belonging to knowledge-relatee seg- ments (lklg). Figure 3 reports the results. The results indicate that our model could learn the underlying structure style of both datasets, with the great difference of pkly and lklg before and after fine-tuning as evidence. After fine-tuned with Wizard data,
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annotated Wiarrd DOU
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Figure 3: The effect of fine-tuning on different data.
8

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317 pklg drops to 0.26 while the lkig grows up a bit, indicating that the knowledge-relater segments 318 generated by our model are fewer and longer, which tallies with the fact that the responses in Wizard are probably directly copied from background knowledge. However, after CMU_ __D data is fed to 320 the model, both pklg and Ikig shrinks drastically, which agrees with the fact that crowd-soureing workers converse more liberally online and the responses are less relevant to background knowledge.
4.4 Results on Learning Content Style
Table Evaluation results on sentiment control. Numbers in bold mean that the improvement to the best performing baseline is statistically significant (I-test with p-value < 0.05).
Wizard Seen
Wizard Unseen
CMU
DoG
Models
positive
negative
positive
negative
positive
negative
Fl
Acc
Fl
Ace
Fl
Acc
Fl
Ace
FI
Acc
Fl
Acc
ECM
10.5
55.8
10.2
60.7
10.1
55.7
10.1
57.6
7.6
41.5
8.3
55.4
DialoGPT
12.1
54.1
12.1
46.9
12.0
56.0
12.0
45.0
9.2
44.9
9.2
55.1
Our Model
19.7
70.3
19.2
70.7
19.4
73.1
19.2
69.9
12.7
74.8
12.2
68.0
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We further investigate whether the proposed model could express knowledge with the desired sentiment. Specifically, we introduce two sets of style adapters to endow knowledge expression in two different sentiments, namely positive and negative. So in this scenario, is required that responses are not only coherent with context but also limited in positive negative sentiment. To apply ECM on knowledge- groddddd conversation, we label the sentiment category for each response with classifier pre-trained on the SST 27 training set. For DialoGPT, we similarly annotate each response with a sentiment category and append the sentiment token before the context tokens. The evaluation results is shown in Table 3 We can conclude that: (1) The proposed model outperforms
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all baseline models in terms of all metrics, which indicates that our model can control the sentiment of knowledge expression and guarantee high quality of the generated responses; (2) Simply adding sentiment indicating token at the beginning of the sequence can not effectively control the style of
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knowledge expression, as the performance of DialoGPT on sentiment control is poor; (3) Although
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ECM is designed for sentiment control, it still fails to perform well in this task, proving that sentiment
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control in the weelleedgoouuddee conversation is rather difficult. Besides, ECM can only control
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the sentiment of the whole response but is helpless to manage every knowledge-related segment at a more refined level.
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Conclusions
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We explore knowledge expression in knowledge-grounde conversation and break down the ex- pression style of a response into the structure of the response (structure style) and the style of the content in each part (content style). We propose a variational segmentation- generation model to discover the underlying expression style in response. Specifically, we introduce two latent variables
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to model these two aspects of expression style respectively and induce an evidence lower bound of the likelihood. Evaluation results on two benchmarks of the task indicate that our model can learn the structure style with little cost and generate responses in desired content style without any data.
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Broader Impact
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Enabling an open-domain dialogue system to automatically detect and discover the underlying structural pattern of sentence is of great significance. This process is destined to be hailed
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milestone on the way to thoroughly reveal the essential nature of open-domain dialogue. Capable of handling different expression styles, positive or negative, casual or serious, our work implies that we
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are now much closer to the final destination of constructing an artificial intelligent dialogue system that could communicate freely with human being, which is beyond the wildest dream of most Al and PLP researchers. In the future, we heartily look forward to seeing advanced methods or ideas based 356 on our work, and we expect the appearance of related industrial projects and applications to benefit
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the people and the public.

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Checklist
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1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's
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contributions and scope? [Yes]
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(b) Did you descrihe the limitations of your work? [No]
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(c) Did you discuss any potential negative societal impacts of your work? [No]
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
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2. If you are including theoretical results...
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(a) Did you state the full set of assumptions of all theoretical results? [Yes]
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(b) Did you include complete proofs of all theoretical results? [Yes]
3. If you experiments..
(a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)? [Yes]
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(b) Did you specify all the training details (e.g., data splits. hyperparameters, how they were chosen)? [Yes]
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(c) Did you report error bars (e.g.. with respect to the random seed after running experi- ments multiple times)? [No]
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(d) Did you include the total amount of compute and the type of resources used (c.g., type of GPUs, internal cluster, or cloud provider)? [Yes]
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4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
(a) If your work uses existing assets, did you cite the creators? [Yes]
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(b) Did you mention the license of the assets? [No]
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(c) Did you include any new assets either in the supplemental material or as a URL? [Yes]
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(d) Did you discuss whether and how consent was obtained from people whose data you're using/curating? [Yes]
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(e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [N/A]
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5. If you used crowdsourcing or conducted research with human subjects...
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(a) Did you include the full text of instructions given to participants and screenshots, applicable? [Yes]
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(b) Did you describe any potential participant risks, with links Institutional Review Board (IRB) approvals, if applicable? [No]
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(c) Did you include the estimated hourly wage paid to participants and the total amount
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spent on participant compensation? [No]
12


Reviews:
[{"review_id": "nZe1Vr_h3Cg", "reviewer": "Reviewer_hdNj", "summary": "The focus of this research piece is on the introduction of a new model for knowledge-grounded open domain dialogue, that can handle different styles in generation.\nThe authors identified that in NLG there are two aspects of generation, the knowledge-related, which is based on the prior knowledge and is factual, and the knowledge-irrelevant, which gives the style and the structure of the rest of the generation.\nConcretely the authors propose an encoder-decoder model where the three (or more) decoders are available, one based on context, other on prior knowledge and a third or more models which are trained on specific generation style.\nA module indicator is trained to choose which model needs to be used. \nThe model is compared against BART and ZRKGC on  Wizard of Wikipedia and CMU_DoG in terms of F1, Distinct unigrams and bigrams showing improvements over both models training with Reddit and Reddit + 10% of annotated data.\nFurthermore, the model is compared against DialoGPT and ECM in terms of sentiment control generation showing a significant difference compared to its competitors", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Overall a good paper. The motivation is clear, although authors should clarify which aspects of the whole model are in fact novel. \nAny NLG paper is meant to have human evaluation since no reliable metric is available. Why didn't the authors include an analysis of that nature as the ZRKGC did?\nWhy PPL is also missing in automatic evaluation as is included in ZRKGC paper?\n\nMissing references\n\nCTRL: A Conditional Transformer Language Model for Controllable Generation (Keska et al 2019) could be a reasonable competitor on the  sentiment control generation task", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes, it seems appropriate", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2"}, "extra_text": {}}, {"review_id": "bxRbTvrwShQ", "reviewer": "Reviewer_MMWY", "summary": "This paper tackles the knowledge grounded dialogue generation task. The paper primarily explores two aspects of knowledge expression: (1) structure of the response, and (2) style of the content. Hence, the proposed model introduces two sequential latent variables to pre-trained encoder-decoder models like BART. The paper proposes a segmentation based generation model which first predicts if the current token to be generated belongs to the previous segment or not and if not then it predicts the type of the new segment. Segment types are knowledge-related (conditions on the knowledge) or knowledge-irrelevant (conditions on the dialogue context).\n\nThe paper proposes a novel model that explores the expression of knowledge in grounded conversations. The proposed model leverages pre-trained encoder-decoder models and extends their ability by adding two latent variables: one for representing structure and the other for style. Additionally, the proposed model has a variational segmentation-based decoder.\n", "questions": "", "limitations": "", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "**Originality:** Knowledge grounded conversation response generation is a well-established task. The paper proposes a novel idea of using knowledge-related or knowledge-irrelevant segments to generate responses. The proposed model is novel especially because it does not rely on using the vanilla pre-trained encoder-decoder model but suggests a useful extension to the model by introducing two latent variables and optimizing the model using a variational approach. Additional papers to cite are:\n\n[1*] ​​Ghazvininejad, Marjan, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, and Michel Galley. \"A knowledge-grounded neural conversation model.\" In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1. 2018.\n\n[2*] Prabhumoye, Shrimai, Kazuma Hashimoto, Yingbo Zhou, Alan W. Black, and Ruslan Salakhutdinov. \"Focused Attention Improves Document-Grounded Generation.\" In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4274-4287. 2021.\n\n[3*] Shuster, Kurt, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. \"Retrieval Augmentation Reduces Hallucination in Conversation.\" arXiv preprint arXiv:2104.07567 (2021).\n\n**Quality:** Although the proposed model is exciting, the paper lacks a thorough evaluation. The paper has reported results only on F1 and D1/D2 metrics. Human evaluation is reported in the supplementary material but it is performed only by 3 annotators. The paper does not report Bleu, METEOR or Rouge metrics which are very important to evaluate generated text. The paper had the opportunity to perform further interesting fine-grained evaluation such as accuracy of predicting segment boundaries and accuracy of predicting segment types to better understand the contribution of the two latent variables. The paper also does not report numbers by training the proposed model on the whole Wizard/CMU_DoG dataset. Since the focus of the paper is to solve knowledge-grounded conversations, this result would add value to the paper. If the focus of the paper is only on few-shot/zero-shot cases then this should be made more explicit in title, abstract and introduction. Overall, it is not clear if the proposed model is faithful to the knowledge provided in the documents/paragraphs. Given the lack of a comprehensive evaluation, it is hard to appreciate the performance of the model in comparison with others.\n\n**Clarity:** The paper is clearly written. More details about implementation, algorithm as well as a case study is provided in the supplementary section.\n\n**Significance:** In Table 2, we see a higher gain for the ZRKGC model for D1/D2 metrics on the Wizard dataset when going from zero-shot case to 10%-annotated case. Hence, the claim on Line 293 may need to be revisited.\n\n**Review after Rebuttal**\n\nThe authors have promised to add details about additional automated metrics, revised ethics statement, results of additional human evaluation which measures the faithfulness of the generated response to the knowledge and a clarification of their goal in the introduction. I am leaning towards acceptance if these changes are made. I have updated my score to reflect the same.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The “Broader Impact” section does not talk about the negative impact of this technology. Overall, knowledge grounded conversation models have the ability to spread fake news. Especially the proposed model which can perform well in zero-shot settings. If the document that is used for grounding is not factually correct, then the model will generate factually incorrect/fake responses. Additionally, the proposed model has the capability of controlling style (positive/negative is reported in the paper). This can be used to further tune the responses according to the target audience.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "5"}, "extra_text": {}}, {"review_id": "WCsGRN8uQuB", "reviewer": "Reviewer_gpDA", "summary": "This paper explores the knowledge expression in knowledge-grounded conversation on two benchmark datasets - Wizard of Wikipedia(Wizard) and CMU Document Grounded Conversation(CMU_DoG). The authors propose a novel variational segmentation-based model to control and capture the expression style by using a few examples. The proposed model outperforms the SOTA on both datasets. \n", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n- The introduction of the latent variables to capture the style is reasonably intuitive and well-motivated.\n- Relevant baselines have been used for this task.\n- SOTA on both the datasets, which were also found to be statistically significant for few-shot learning.\n- The authors provide the corresponding code and detailed description of hyperparameters which would be essential for reproducibility if open-sourced. \n\nWeakness:\n\n- The paper is not well-written and lacks details making it difficult to understand; see comments below.\n- It is not clear if the model uses pre-trained BART or just the model architecture as the backbone (line 141, 262). If the latter, it is not clear why the pre-trained weights were not considered. \n- If a pre-trained BART is used, it seems it would create a mismatch between pre-training and fine-tuning, since the [EOS] token does not appear during pre-training. Could the authors confirm if the [EOS] token is used as a separator and not the [SEP] token in equation 1.\n- Pre-training on Reddit corpus is also not adequately motivated. Why was this particular dataset used for pre-training and the similarity of this dataset with the downstream tasks? An ablation study depicting the model trained from scratch on the respective datasets could provide more information if the improvement is because of more training data or the introduction of latent variables. \n- An ablation study is required to understand the importance of each of the latent variables introduced for this task.\n- Detailed information about the adapters in the decoder is missing. Figure 1 could also be improved by showing the decoder as well as the adapters used. An ablation study with or without the adapters in the decoder layer is necessary to understand their importance. Also, it should be explicitly mentioned if all the models in Tables 2 and 3 use these adapters.\n- While the authors claim that the model generates the responses, it is unclear if it is indeed an autoregressive model and the output is generated word by word. The corresponding loss function could provide more details. If the whole task is a generation setup, corresponding NLG metrics are not considered. Some model-generated examples would provide more insight. \n- Y-axis for Figure 2 is misleading and provides a hyperbole of the findings. It is not clear if the authors also experimented by incorporating 100% training data and how the results changed.\n- Pklg and lklg are not properly motivated as metrics and their correct interpretation is not provided. Relevance of background knowledge would be well justified by corresponding data analysis. \n\n\nQuestions:\n- On line 146, how do the authors handle sequences of variable length? Are all the sequences of $l_{k}$? Do the authors use padding? \n- What does delta in Equation 2 and 8 signify? Does it refer to the Dirac delta function?\n- Similarly, could the authors correctly specify what does f mean on line 205 and 210\n\nSuggestions/Comments:\n- Please be careful of the formatting. Eg. line 134, 136, 268, 298 \n- Line 40: Structure style -> The structure style\n- Line 41: even the -> even if the\n- Line 72: Exploration the -> Exploration of the\n- Line 113: each is other -> each is either\n- Line 204: Base -> Based\n- Line 206: keeps -> is kept\n- Line 287: leverage -> leveraging\n- Line 288: In low -> In the low\n- Line 293: quickly -> quick\n- Line 316: After fine-tuned -> fine-tuning\n- Line 321: to -> to the\n- Please provide the y-axis for Figure 3.\n\nThe results seem promising, however, the paper could be improved in the presentation and more detailed descriptions. \n\nUpdate after rebuttal\n\nThank you authors for answering my questions. Though the results seem promising, I believe the paper needs restructuring a bit and more clarification about the experimental setup as well as the human evaluations. Maintaining the original rating.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes / NA", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "8-10"}, "extra_text": {}}, {"review_id": "6TIFNwTnZaL", "reviewer": "Reviewer_Hbvb", "summary": "The authors study the problem of knowledge-grounded conversation, and propose a segment generation process to better “express” knowledge in the conversation. Specifically, the authors identify that expressing a response can be broken down into two components: the structure of the response and the content style; the former marked by either “knowledge-related” or “knowledge-irrelevant” segments, while the latter is characterized by positive, negative, or other styles of formulating a response. The generation model is based on a normal encoder/decoder architecture, but the decoder is influenced by various introduced latent variables when determining how to generate the next token. A “boundary” indicator informs the decoder when it has reached the boundary of a segment (and must therefore transition), and a “module” indicator determines which structure/segment type to use when generating; these indicators inform the decoder which encoded representations to attend to when generating and are learned via weak supervision. The authors conduct experiments on two knowledge-grounded datasets and show that their model outperforms strong baselines in both zero-resource and low-resource settings, and additionally show that their method is more adaptable to training data (as it can learn more in a low-resource setting than the other models). The authors additionally show that the model is more controllable in a sentiment-control setting, such that sentiment classifiers indicate the controlled responses more effectively model a positive or negative sentiment depending on the setup.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "*Originality*: The method is original and is an interesting approach to response generation (specifically the segment generation component). Related work is adequately cited and it is clear how the work differs from prior contributions; it is important to note that the authors study explicitly the **expression of knowledge**, rather than the selection of knowledge, and thus compare to methods trained in similar fashions (specifically the ZRKGC method for WoW and CMU_DoG)\n\n*Quality*: The submission is technically sound, and claims are well supported via experimental results; the authors display a few interesting analyses of how the model learns (fine-tuning on less annotated data and refashioning of knowledge-related segments). However, without an analysis of the qualitative model outputs, I am a little bit skeptical about the segment generation process → how are we sure that the models responses are coherent if the model is switching between various modules? F1 word overlap only measures so much.\n\n*Clarity*:\n- The paper is well-written and methods/results are clearly outlined and presented in the text.\n- However, the “content style” component of the method is less studied/explained than I would have liked. To start, the majority of the analysis focused explicitly on knowledge expression, but this left out more analysis on the sentiment control, and so that section felt a bit lacking. Additionally, it is not particularly clear what “adapters” were inserted into the decoder layers when in the stylized knowledge regime. Finally, it is not clear why the authors chose to limit their study of stylized generation to the knowledge-relevant sections only; could this have brought additional control over sentiment if there was stylized generation for the knowledge-irrelevant sections?\n\n*Significance*: The authors obtain SOTA results on the two datasets compared to similarly trained baselines in low-resource settings, and indeed the methods appear to learn quicker from less data, although it would have been interesting to see the model’s performance when trained on the full set of data.\n\n\n***EDIT Following Discussion Period***\n\nAfter reading other reviews (and further discussion with other reviewers), I am revising my score to be 6. I agree with others' sentiments that a more thorough evaluation is required to adequately measure the efficacy of the method (e.g., a human evaluation), and indeed in its current state it is unclear what the true motivation/focus of the paper is (few/zero-shot knowledge grounding or knowledge grounding in general).\n\n\n***2nd EDIT Following Discussion Period***\n\nThe authors have done an appropriate job of addressing other reviewers concerns (as well as mine) in further discussion, so I am inclined to revise my score back to a 7. I thank the authors for their willingness to engage in discussion and provide additional details where necessary.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "As noted in the main review, I would have liked to have seen some justification for not applying the style adapters in the knowledge-irrelevant regime (even if it’s as simple as maintaining elegance/less computational costs)", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2.5 hours"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper explores knowledge-grounded conversation and proposes a new method for better expressing knowledge in conversation by exploiting latent variables encoding either the structure of the response or the style of the content. The reviewers generally think that the approach is intuitive and well-motivated, that it performs well and that the results are important. There were some concerns about the human evaluation in particular, but these were addressed in the author response. I would encourage the authors to be much more civil in future discussions with reviewers and area chairs. As it stands, this paper is borderline, with one reviewer saying the paper is not well-written while two other reviewers argue the exact opposite.
Decision: Reject