Original: Toward Knowledge-Enriched Conversational Recommendation System
Revised: Toward Knowledge-Enriched Conversational Recommendation Systems

Original: Anonymous ACL submission

Original: Abstract
Revised: Abstract

Original: Conversational Recommendation System recommend items through language based interactions with users.
Revised: Conversational Recommendation Systems recommend items through language based interactions with users.

Original: In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entitiesloss, which encourages the generated utterances to mention concepts related to the itembeing recommended, such as the genre or director of a movie.
Revised: In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie.

Original: We also propose an alignmentloss to further integrate KG entities into the response generation network.
Revised: We also propose an alignment loss to further integrate KG entities into the response generation network.

Original: Experiments on thelarge-scale REDIAL dataset demonstrate thatthe proposed system consistently outperformsstate-of-the-art baselines.
Revised: Experiments on the large-scale REDIAL dataset demonstrate that the proposed system consistently outperforms state-of-the-art baselines.

Original:  Introduction
Revised:  Introduction

Original: Conversational recommendation systems (CRS)have received increasing attention from the NLPcommunity (Li et al., 2018; Chen et al., 2019; Zhouet al., 2020a; Sarkar et al., 2020; Liu et al., 2020;
Revised: Conversational recommendation systems (CRS) have received increasing attention from the Natural Language Processing community (Li et al., 2018; Chen et al., 2019; Zhou et al., 2020a;

Original:  Zhou et al., 2020b; Hayati et al., 2020).
Revised: Sarkar et al., 2020; Liu et al., 2020; Zhou et al., 2020b; Hayati et al., 2020).

Original: CRS aimsto recommend items, such as movies or songs, innaturalistic interactive conversations with the user.
Revised: CRS aims to recommend items, such as movies or songs, in naturalistic interactive conversations with the user.

Original: This interactive form allows the system to providerecommendations tailored to preferences providedby the user at the moment.
Revised: This interactive form allows the system to provide recommendations tailored to preferences provided by the user at the moment.

Original: A crucial issue of CRS is to extract user preferences from the conversation, which often requiresbackground information provided by knowledgegraphs (KGs).
Revised: A crucial issue of CRS is to extract user preferences from the conversation, which often requires background information provided by knowledge graphs (KGs).

Original: As an example, in Figure 1, theuser mentions two movies that belong to the horrorgenre.
Revised: As an example, in Figure 1, the user mentions two movies that belong to the horror genre.

Original: To this end, some existing studies (Chenet al., 2019;
Revised: To this end, some existing studies (Chen et al., 2019;

Original: Zhou et al., 2020a)
Revised: Zhou et al., 2020a)

Original: leverage knowledgegraphs to understand user intentions.
Revised: leverage knowledge graphs to understand user intentions.

Original: We observe that when humans recommend itemsto friends, they usually describe attributes of theitem.
Revised: We observe that when humans recommend items to friends, they usually describe attributes of the item.

Original: For example, to recommend a movie, theymay mention the director or actors.
Revised: For example, to recommend a movie, they may mention the director or actors.

Original: Such information can be easily extracted from the knowledgegraph, but has not been utilized by existing approaches.
Revised: Such information can be easily extracted from the knowledge

Original: To emulate naturalistic conversations, Hi! What kind of movies do you like? Hi, I am looking for a movie recommendation.
Revised: [Table]

Original: When I was younger, I really enjoyed the A Nightmare on Elm Street (1984) I also enjoyed watching The Last House on the Left(1972). Oh, you like scary movies? I recently watched Happy Death Day(2017) .
Revised: [Figure]

Original: It was good for a new “scary movie”. Chatbot User User
Revised: graph, but has not been well utilized by existing approaches.

Original: Chatbot we propose a Bag-of-Entities (BOE) loss, which encourages the generated utterances to mention concepts related to the item.
Revised: To emulate naturalistic conversations, we propose a Bag-of-Entities (BOE) loss, which encourages the generated utterances to mention concepts related to the item.

Original: Moreover, we propose an alignment loss that ties the word embeddings to the entity embeddings.
Revised: Moreover, we propose an alignment loss that ties the word embeddings to the entity embeddings.

Original: Experiments demonstrate that the proposed two losses improve model performance.
Revised: Experiments demonstrate that the proposed two losses improve model performance.

Original: The proposed the Knowledge-Enriched Conversational Recommendation System (KECRS) consistently outperforms state-of-the-art CRSs on the large-scale REDIAL dataset (Li et al., 2018).
Revised: The proposed the Knowledge-Enriched Conversational Recommendation System (KECRS) consistently outperforms state-of-the-art CRSs on the large-scale REDIAL dataset (Li et al., 2018).

Original:  Related work
Revised:  Related work

Original: We briefly review work on conversational recommendation systems and conversational characters in e-commerce settings.
Revised: We briefly review work on conversational recommendation systems and conversational characters in e-commerce settings.

Original: A number of works on conversational recommendation systems focus solely on interactive recommendation rather than language understanding (Christakopoulou et al., 2016, 2018; Sun and Zhang, 2018; Zhang et al., 2018; Lei et al., 2020a,b; Zou et al., 2020; Xu et al., 2021).
Revised: A number of works on conversational recommendation systems focus solely on interactive recommendation rather than language understanding (Christakopoulou et al., 2016, 2018; Sun and Zhang, 2018; Zhang et al., 2018; Lei et al., 2020a,b; Zou et al., 2020; Xu et al., 2021; Zhang et al., 2022).

Original: In contrast, a second category of works aim to provide both accurate interactive recommendations and generate naturaland human-like responses (Li et al., 2018; Chen et al., 2019; Zhou et al., 2020a; Sarkar et al., 2020; Liu et al., 2020; Zhou et al., Recommendation Module
Revised: In contrast, a second category of works aims to provide both accurate interactive recommendations and generate natural

Original: Entity Sequence 𝑬
Revised: Entity Sequence 𝑬

Original: Look Up 𝐻
Revised: Look Up 𝐻

Original: Self Attention
Revised: Self Attention

Original: Hidden Representation 𝒄 𝑬
Revised: Hidden Representation 𝒄 𝑬

Original: Graph Neural
Revised: Graph Neural

Original: Network
Revised: Network

Original: Entity Embedding 𝑯
Revised: Entity Embedding 𝑯

Original: Knowledge graph
Revised: Knowledge graph

Original: Matching 0.
Revised: Matching 0.

Original: Encoder
Revised: Encoder

Original: Decoder
Revised: Decoder

Original: Vocabulary Embedding 𝑾 𝑬
Revised: Vocabulary Embedding 𝑾 𝒗

Original: 𝒎

Original: Conversation context
Revised: Conversation context

Original: Entity
Revised: Entity

Original: Selection
Revised: Selection

Original: Generation
Revised: Generation

Original: Hidden Representation 𝒄 𝑬
Revised: Hidden Representation 𝒄 𝑬

Original: KG Entity Distribution
Revised: KG Entity Distribution

Original: Vocabulary Distribution
Revised: Vocabulary Distribution

Original: Embedding
Revised: Embedding

Original: Alignment
Revised: Alignment

Original: Response Generation Module
Revised: Response Generation Network

Original: Predict : You’ll have to check out It (2017) if you like horror movies with murder mystery.
Revised: Predict : You’ll have to check out It (2017) if you like horror movies with murder mystery.

Original: 2020b;
Revised: and human-like responses (Li et al., 2018; Chen et al., 2019; Zhou et al., 2020a; Sarkar et al., 2020;

Original:  Hayati et al., 2020).
Revised: Liu et al., 2020; Zhou et al., 2020b; Hayati et al., 2020).

Original: Finally, research onconversational characters for e-commerce has thebroad goal of building  complete shopping assistantthat can answer a variety of questions in addition torecommendation (Li et al., 2017; Yang et al., 2018;
Revised: Finally, research on conversational characters for e-commerce has the broad goal of building a complete shopping assistant that can answer a variety of questions in addition to recommendation (Li et al., 2017; Yang et al., 2018;

Original: Fu et al., 2020; Song et al., 2021).
Revised: Fu et al., 2020; Song et al., 2021).

Original:  Approach
Revised:  Approach

Original: The overall goal of a conversational recommendation system is to identify an item (e.g., a movie, asong, or a piece of merchandise) that the user willlikely interact with and suggest the item to the userin the form of natural language conversations.
Revised: The overall goal of a conversational recommendation system is to identify an item (e.g., a movie, a song, or a piece of merchandise) that the user will likely interact with and suggest the item to the user in the form of natural language conversations.

Original: Formally, we represent the historic conversation
Revised: Formally, we represent the historic conversation

Original: X = ⟨ x 1 , x 2 , ..., x n ⟩ as a sequence of n utterancesx i .
Revised: X = ⟨ x 1 , x 2 , ..., x n ⟩ as a sequence of n utterances x i .

Original: The knowledge graph G = { ( v h , r, v t ) } is a setof relationships r between the head entity v h ∈ Eand the tail entity v t ∈ E .E is the set of all entities.
Revised: The knowledge graph G = { ( v h , r, v t ) } is a set of entities E and a set of relationships r between the head entity v h ∈ E and the tail entity v t ∈ E .

Original: The conversational recommendation task is topredict the next utterance x n +1 using the recommendation network f
Revised: The conversational recommendation task is to predict the next utterance x n +1 using the recommendation network f

Original: ( X, G ) and the response generation network g ( X, G , f
Revised: ( X, G ) and the response generation network g ( X, G , f

Original: ( X, G )) .
Revised: ( X, G )) .

Original: f ( X, G ) predicts the next item to recommend to the user,whereas g ( X, G , f ( X, G )) predicts the utterancex n +1 one word at a time.
Revised: f ( X, G ) predicts the next item to recommend to the user, whereas g ( X, G , f ( X, G )) predicts the utterance x n +1 one word at a time.

Revised: Figure 2 shows the overall structure of our proposed method, the Knowledge-Enriched Conversational Recommendation System (KECRS).

Original: 3. Recommendation Network
Revised: 3. Recommendation Network

Original: From the conversational history X , we first extractall entities  and sequence them according to theirposition in X . This yields a sequence of K entities.
Revised: First, we exhaustively match each word in the conversational history X with the name of each entity in the KG. In this way, we identify K entities from the history and sequence them according to their original positions.

Original: Next, we extract entity embeddings from a graphconvolutional network, R-GCN (Schlichtkrull et al.,2017), which encodes the entire knowledge graph.
Revised: Next, we apply a graph convolutional network, R-GCN (Schlichtkrull et al., 2017) to encode the entire KG and obtain embeddings for each KG entity node.

Original: We concatenate the D -dimensional entity embeddings as matrix H e ∈ R K × D .
Revised: The D -dimensional entity embeddings of the K entity form the matrix H E ∈ R K × D .

Original: Subsequently, we apply an attention operation where the attention vector α is computed by a 2-layer MLP.
Revised: Subsequently, we apply an attention operation where the attention vector α is computed by 2 fully connected (FC) layers.

Original: [Equation]
Revised: [Equation]

Original: where W q and W k are learnable parameters.
Revised: where W q and W k are learnable parameters.

Original: The resulted c E ∈  D is a condensed representation of entities appearing in the conversational history.
Revised: The resulting c E ∈ R D is a condensed representation of entities appearing in the conversational history.

Original: The recommendation module classifies c e directly into one of the items.
Revised: The recommendation module classifies c E directly into one of the items.

Original: We directly take the entity embedding e i from the R-GCN network as the representation of the item.
Revised: We directly take the entity embedding e i from the R-GCN network as the representation of the item.

Original: The probability of recommending item i is computed with softmax:
Revised: The probability of recommending item i is computed with softmax:

Original: [Equation]
Revised: [Equation]

Original: The module is trained using the cross-entropy loss.
Revised: The module is trained using the cross-entropy loss.

Revised: To avoid the model recommending the same movie that the user might have just mentioned, we only consider as a ground-truth recommendation the movie that is first time to be mentioned by the recommender in the conversation.

Original: 3. Response Generation Network
Revised: 3. Response Generation Network

Original: The response generation module predicts the utterance to the user word by word.
Revised: The response generation module predicts the utterance to the user word by word.

Original: We use the classic encoder-decoder Transformer architecture, where the encoder encodes the entire conversational history word by word.
Revised: We use the classic encoder-decoder Transformer architecture (Vaswani et al., 2017), where the encoder encodes the entire conversational history word by word.

Original: At decoding time step j , the output of the Transformer decoder s j is concatenated with the entity representation c E and goes through two fully connected layers before the softmax function.
Revised: At decoding time step j , the output of the Transformer decoder s j is concatenated with the entity representation c E and goes through two fully connected layers before the softmax function.

Original: The probability distribution over the vocabulary is
Revised: The probability distribution over the vocabulary is

Original: [Equation]
Revised: [Equation]

Original: where W v is the word embedding matrix sharedwith the encoder.
Revised: where W v is the word embedding matrix shared with the encoder.

Original: W a is a trainable linear projection to align the dimensions, and b is the bias.
Revised: W a is a trainable linear projection to align the dimensions, and b is the bias.

Original: We train the module using cross-entropy at everydecoder time step.
Revised: We train the module using cross-entropy at every decoder time step.

Original: It is worth noting that, to explicitly represent candidate items, in the vocabulary we create separatetokens for them.
Revised: To separate movie names from other words in the conversation, for every movie name we create specialized tokens in the vocabulary.

Original: For example, the token for themovie name It is separate from the word token it .
Revised: For example, the token for the movie name It is separate from the word token it .

Revised: This is feasible as the dataset,

Revised: REDIAL, has explicitly represented movie names with special strings.

Original: 3. Bag-of-Entities Loss
Revised: 3. Bag-of-Entities Loss

Original: Although the response generation module trainedusing per-step cross-entropy is capable of recommending items, it rarely mentions concepts relatedto the recommended item.
Revised: Although the response generation module trained using per-step cross-entropy is capable of recommending items, it rarely mentions concepts related to the recommended item.

Original: We postulate that mentioning related entities will produce natural conversations.
Revised: We postulate that mentioning related entities will produce natural conversations.

Original: For example, when recommending themovie It , one may want to mention that it is a horrormovie based on a book by Stephen King.
Revised: For example, when recommending the movie It , one may want to mention that it is a horror movie based on a book by Stephen King.

Original: For this purpose, we introduce the Bag-of-Entity(BOE) loss, which encourages the decoder state[ s j ; c E ] to contain additional information aboutfirst-order neighbors of the ground-truth recommendation on the KG.
Revised: For this purpose, we introduce the Bag-of-Entity (BOE) loss, which encourages the decoder state [ s j ; c E ] to contain additional information about first-order neighbors of the ground-truth recommendation on the KG.

Original: First, at every time step, we compute a scorer j ∈ R M for all M entities in the knowledge graph,
Revised: First, at every time step, we compute a score r j ∈ R M for all M entities in the knowledge graph,

Original: [Equation]
Revised: [Equation]

Original: where H contains the embeddings of all KG entities, as produced by the R-GCN.
Revised: where H contains the embeddings of all KG entities, as produced by the R-GCN.

Original: W b is a trainablematrix for dimension alignment and b ent the bias.
Revised: W b is a trainable matrix for dimension alignment and b ent the bias.

Original: As we do not constrain exactly which word inthe response should contain the information, wesum up the word-level scores and then apply thecomponent-wise sigmoid function.
Revised: As we do not constrain exactly which word in the response should contain the information, we sum up the word-level scores and then apply the component-wise sigmoid function.

Original: The probabilitythat entity m is mentioned in the response is thus
Revised: The probability that entity m is mentioned in the response is thus

Original: [Equation]
Revised: [Equation]

Original: where L is the length of the response and r jm isthe m th component of r j .
Revised: where L is the length of the response and r jm is the m th component of r j .

Original: We apply a binary cross-entropy loss for each
Revised: We apply a binary cross-entropy loss for each

Original: KG entity.
Revised: KG entity.

Original: The ground-truth label is 1 if the entityis a first-order neighbor of the recommended itemon the knowledge graph and 0 otherwise.
Revised: The ground-truth label is 1 if the entity is a first-order neighbor of the recommended item on the knowledge graph and 0 otherwise.

Original: 3. Aligning Word and Entity Embeddings
Revised: 3. Aligning Word and Entity Embeddings

Original: We create two types of tokens in the vocabulary
Revised: We create two types of tokens in the vocabulary

Original: V of the response generation network.
Revised: V of the response generation network.

Original: The firsttype corresponds to a plain word appearing in theconversation text.
Revised: The first type corresponds to a plain word appearing in the conversation text.

Original: The second type represents an entity that appears in the conversation and in the knowledge graph.
Revised: The second type represents an entity that appears in the conversation and in the knowledge graph.

Original: To tie the token embeddings of the second type to the R-GCN encoding of the knowledge graph, we propose the alignment loss.
Revised: To tie the token embeddings of the second type to the R-GCN encoding of the knowledge graph, we propose the alignment loss.

Original: For a conversation, we use the entity representation c E in  (1) to represent all entities in the conversation and calculate the similarity score between c E and each word embedding,
Revised: For a conversation, we use the entity representation c E in Eq. (1) to represent all entities in the conversation and calculate the similarity score between c E and each word embedding,

Original: [Equation]
Revised: [Equation]

Original: where W v [ E ] is the matrix resulted from selecting the rows of W v corresponding to entity tokens only.
Revised: where W v [ E ] is the matrix resulting from selecting the rows of W v corresponding to entity tokens only.

Original: W c is a trainable matrix and b align is the bias.
Revised: W c is a trainable matrix and b align is the bias.

Original: The alignment loss is the mean square error between the s and an indicator vector q ∈ { 0 , 1 } | E | .
Revised: The alignment loss is the mean square error between the s and an indicator vector q ∈ { 0 , 1 } | E | .

Original: [Equation]
Revised: [Equation]

Original: Specifically, if an entity e exists in the conversation, the corresponding component of q is set to 1.
Revised: Specifically, if an entity e exists in the conversation, the corresponding component of q is set to 1.

Original: Otherwise, the component is 0.
Revised: Otherwise, the component is 0.

Original: Finally, to learn the parameters of generation module, we minimize the following objective function:
Revised: Finally, to learn the parameters of generation module, we minimize the following objective function:

Original: [Equation]
Revised: [Equation]

Original: where λ 1 and λ 2 are two hyperparameters.
Revised: where λ 1 and λ 2 are two hyperparameters.

Original: In the testing procedure, the probability distribution over the vocabulary at time step j is calculated as follows,
Revised: In the testing procedure, the probability distribution over the vocabulary at time step j is calculated as follows,

Original: [Equation]
Revised: [Equation]

Original: where λ 3 is a hyperparameter.
Revised: where λ 3 is a hyperparameter.

Revised: [Table]

Original:  Experiments 
Revised:  Experiments 4. Dataset

Original: 4. Dataset

Original: We use the REDIAL dataset (Li et al., 2018), which includes 10,006 conversations and 182,150 utterances related to 51,699 movies.
Revised: We use the REDIAL dataset (Li et al., 2018), which includes 10,006 conversations and 182,150 utterances related to 51,699 movies.

Original: Following (Li et al., 2018;
Revised: Following (Li et al., 2018;

Original: Chen et al., 2019; Zhou et al., 2020a), we split REDIAL into training, validation, and testing sets with the ratio 8:1:1.
Revised: Chen et al., 2019; Zhou et al., 2020a), we split REDIAL into training, validation [Table] and testing sets with the ratio 8:1:1.

Original: We build the knowledge graph, TMDKG, from The Movie Database 1 , which contains 15822 entities and 15 types of relations.[Table]
Revised: We build the knowledge graph, TMDKG, from The Movie Database 1 , which contains 15822 entities and 15 types of relations.

Original: 4. Evaluation Metrics
Revised: 4. Evaluation Metrics

Original: Following (Chen et al., 2019; Zhou et al., 2020a),we use Distinct n-gram (n=2, 3, 4) to measure thediversity of generated responses.
Revised: Following (Chen et al., 2019; Zhou et al., 2020a), we use Distinct n-gram (n=2, 3, 4) to measure the diversity of generated responses.

Original: To better evaluate the performance of generated responses, weadopt human evaluation.
Revised: To better evaluate the performance of generated responses, we adopt human evaluation.

Original: We randomly sample 100multi-turn conversations from the test set and invitethree annotators to score responses generated bydifferent models from the following aspects: 1) Fluency : whether responses are fluent;2)
Revised: We randomly samplemulti-turn conversations from the test set and invite three annotators to score responses generated by different models from the following aspects: 1) Fluency : whether responses are fluent;2)

Original: Relevancy :whether responses are correlated with contexts;3)
Revised: Relevancy : whether responses are correlated with contexts;3)

Original: Informativeness : whether responses contain richinformation of recommended items.
Revised: Informativeness : whether responses contain rich information of recommended items.

Original: Each aspect israted in [0 , 3] , and final scores are the average of allannotators.
Revised: Each aspect is rated in [0 , 3] , and final scores are the average of all annotators.

Original: For all evaluation metrics, the highervalue indicates better performances.
Revised: For all evaluation metrics, the higher value indicates better performances.

Original: 4. Baseline Methods
Revised: 4. Baseline Methods

Original: We compare KECRS with the following baselinemethods: 1) HRED-CRS (Li et al., 2018):
Revised: We compare KECRS with the following baseline methods: 1) HRED-CRS (Li et al., 2018):

Original: This is abasic CRS based on HRED(Serban et al., 2016); 2)
Revised: This is a basic CRS based on HRED(Serban et al., 2016); 2)

Original: Transformer (Vaswani et al., 2017):
Revised: Transformer (Vaswani et al., 2017):

Original: This is a basictransformer model that generates responses onlyfrom utterance text and does not contain a separaterecommendation module; 3) KBRD (Chen et al.
Revised: This is a basic transformer model that generates responses only from utterance text and does not contain a separate recommendation module; 3) KBRD (Chen et al., [Table]

Original: ,2019):This is a knowledge-based CRS that employs
Revised: 2019):This is a knowledge-based CRS that employs

Original: DBpedia to understand the user’s intentions andleverage KG information as a bias for generation;4) KGSF
Revised: DBpedia to understand the user’s intentions and leverage KG information as a bias for generation; 4) KGSF

Original: (Zhou et al., 2020a):
Revised: (Zhou et al., 2020a):

Original: This method exploits both entity-oriented and word-oriented KGsto enrich the data representations.
Revised: This method exploits both entity-oriented and word-oriented KGs to enrich the data representations.

Original: It adopt two
Revised: It adopts two

Original: KG-enriched decoder layers for the generation.
Revised: KG-enriched decoder layers for the generation.

Original: 4. Results and Discussion
Revised: 4. Results and Discussion

Original: The automatic and human evaluation results of different methods are shown in Table 1.
Revised: The automatic and human evaluation results of different methods are shown in Table 1.

Original: We notethat Transformer performs better than HRED-CRS,which demonstrates that Transformer is powerful tounderstand and generate natural language.KBRDperforms better than Transformer, because it adds User: Hi there, how are you?
Revised: We note that Transformer performs better than HRED-CRS, which demonstrates that Transformer is powerful to understand and generate natural language.

Original: Recommender: I’m doing great, how about you?

Original: User:

Original: Fine thanks.

Original: I ’d love to see something similar to There’s

Original: Something

Original: About Mary .

Original: That movie always cracks me up !

Original: Transformer: I haven’t seen that one. KBRD: I haven’t seen that one.
Revised: KBRD performs better than Transformer, because it adds a vocabulary bias to fuse knowledge from KG into the generated responses.

Original: What about

Original: My Best Friend’s Wedding ? KGSF: I recommend The Other Woman ? KECRS (Ours):
Revised: Among all the baseline models, KGSF generates the most diverse responses, by exploiting both TMDKG and ConceptNet (Speer et al., 2017).

Original: I love Meet the Parents . It ’s a classic . It’s a little older , but still funny and romantic . a vocabulary bias to fuse knowledge from KG into the generated responses.
Revised: The potential reason is that

Original: Among all the baseline models, KGSF generates the most diverse responses, by exploiting both TMDKG and ConceptNet.

Original: The potential reason is that KGSF employs two additional KG-based attention layers to make the generative model focus more on items and relevant entities in TMDKG and ConceptNet.
Revised: KGSF employs two additional KG-based attention layers to make the generative model focus more on items and relevant entities in TMDKG and ConceptNet.

Original: Moreover, the proposed KECRS model outperforms all baseline methods with a large margin in terms of all evaluation metrics.
Revised: Moreover, the proposed KECRS model outperforms all baseline methods with a large margin in terms of all evaluation metrics.

Original: This demonstrates that the proposed BOE loss and alignment loss can work jointly to better leverage KG and generate more diverse and informative responses.
Revised: This demonstrates that the proposed BOE loss and alignment loss can work jointly to better leverage KG and generate more diverse and informative responses.

Original: For human evaluation, we note that Fluency is relatively higher compared to Informativeness and
Revised: For human evaluation, we note that Fluency is relatively higher compared to Informativeness and

Original: Relevancy for all models.
Revised: Relevancy for all models.

Original: This indicates that responses generated by these models are fluent and can be understood by human judges.
Revised: This indicates that responses generated by these models are fluent and can be understood by human judges.

Original: However, responses generated by baseline models are more likely to be generic responses ( e.g., “I haven’t seen that one”).
Revised: However, responses generated by baseline models are more likely to be generic responses ( e.g., “I haven’t seen that one”).

Original: By including additional supervision signals and aligning embeddings of word and entities, the proposed KECRS model alleviates this issue.
Revised: By including additional supervision signals and aligning embeddings of word and entities, the proposed KECRS model alleviates this issue.

Original: Overall, KECRS can understand the dialogue context and generate fluent, relevant, and informative responses.
Revised: Overall, KECRS can understand the dialogue context and generate fluent, relevant, and informative responses.

Original: 4. Case Study
Revised: 4. Ablation Study

Revised: To better understand effectiveness of each component in KECRS, we study the performances of following two variants of KECRS: 1)

Revised: KECRS

Revised: w/

Revised: o BOE

Revised: , which removes the BOE loss, and 2)

Revised: KECRS w/o align

Revised: which removes the infusion loss.

Revised: Table 2 summarizes the response generation performance in terms of Distinct n-gram (n=2,3,4).

Revised: Distinct n-gram measures the diversity of sentences by calculating the number of distinct ngram in generated responses.

Revised: KECRS outperforms

Revised: KECRS w/o BOE , which indicates the proposed BOE loss can help the model learn to generate responses not only from conversations but also from the knowledge graph.

Revised: Moreover, KECRS w/o align performs poorer than KECRS.

Revised: This indicates that aligning the the word embeddings and entity embeddings also helps improve the model performances.

Revised: Compared with KGSF, both ablated versions of KECRS can achieve better performances in terms of most metrics.

Revised: This again demonstrates that encouraging model to mention concept related to the recommended items and aligning word embeddings with KG entity embeddings both can help model generate more diverse responses.

Revised: 4. Case Study

Original: In Table 2, we present a qualitative comparison of the responses generated by different models.
Revised: In Table 3, we present a qualitative comparison of the responses generated by different models.

Revised: The conversation is selected from the dataset REDIAL.

Original: When the user expresses preferences on " There’s Something About Mary ", KECRS infers the user may like romantic comedy movie.
Revised: When the user expresses preferences on " There’s Something About Mary ", KECRS infers the user may like romantic comedy movie.

Original: Thus, KECRS recommends another romantic comedy movie " Meet the Parents " and provides an informative and natural response "It ’s a classic.
Revised: Thus, KECRS recommends another romantic comedy movie " Meet the Parents " and provides an informative and natural response "It ’s a classic.

Original: It’s a little older , but still funny and romantic" . 
Revised: It’s a little older , but still funny and romantic" . [Table] Something [Table]

Revised:  Conclusions

Revised: In this paper, we propose a novel KnowledgeEnriched Conversational Recommendation System (KECRS).

Revised: Specifically, we develop the Bag-ofEntity (BOE) loss and the alignment loss to improve the response generation performances.

Revised: The experimental results on REDIAL demonstrate that the proposed BOE loss can guide the model to generate more knowledge-enriched responses by selecting entities in KG, and the alignment loss can ties the word embeddings to the entity embeddings.

Revised: Overall, KECRS achieves superior response quality than state-of-the-art baselines.

Revised: For future work, we would like to investigate how to use keywords to conduct the conversation from chit-chat to the recommendation (Zhou et al., 2020b; Liu et al., 2020).

Revised: Moreover, we are also interested in using external knowledge ( e.g., KG) to modify the REDIAL dataset and make responses more relevant to the recommended items (Jannach and Manzoor, 2020).

Revised:  Acknowledgments

Revised: The authors gratefully acknowledge the support from the Alibaba-NTU Joint Research Institute,

Revised: Nanyang Associate Professorship, and the National

Revised: Research Foundation, Singapore (NRF-NRFF132021-0006).

Revised: Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the view of the funding agencies.