C oref D iffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations (2022)

Abstract: Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally. For document-grounded dialog systems, the inter- and intra-document knowledge relations can be used to model such conversational flows. We develop a novel Multi-Document Co-Referential Graph (Coref-MDG) to effectively capture the inter-document relationships based on commonsense and similarity and the intra-document co-referential structures of knowledge segments within the grounding documents. We propose CorefDiffs, a Co-referential and Differential flow management method, to linearize the static Coref-MDG into conversational sequence logic. CorefDiffs performs knowledge selection by accounting for contextual graph structures and the knowledge difference sequences. CorefDiffs significantly outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks. This demonstrates that the effective modeling of co-reference and knowledge difference for dialog flows are critical for transitions in document-grounded conversation.

## introduction document-grounded conversations @xcite @xcite ) is a core class of knowledge-grounded dialogs that leverage text-based knowledge segments from documents to generate informative dialog responses. this task is typically divided into two sub-tasks, given the dialog history @xcite : namely, knowledge selection and response generation. knowledge selection, which determines the content of the generated responses @xcite , is the crucial sub-task for dialog flow management as it leads to the manifestation of knowledge transition @xcite , essential for naturalistic engaging conversations. most existing studies on document-grounded conversations @xcite @xcite treat knowledge selection as a matching problem between the dialog context and individual knowledge segments, independently. however, for document-grounded conversations, we posit that there is an implicit alignment between the background knowledge and conversation logic which can be learned from the underlying structural relationships of the knowledge segments within and between the grounding documents. for example, the conversation in figure 1 exhibits documentlevel topic flow, from science fiction -> star wars-> the empire strikes back, and deep dives into the specifics of the star wars document (turns 2 to 3). to effectively exploit the relationships of the knowledge segments to guide dialog flows would require a thorough comprehension of the intrak1 k2 k3 k4 k5 k6 k7 k1 k2 k3 k4 k5 k6 k7 k1 k2 k3 k4 k5 k6 document discourse structures and inter-document relationships for the knowledge selection process. existing works either ignore such relations (as illustrated in figure 2 (a)), or exploit limited local correlations (as depicted in figure 2 (b)), for example by encoding knowledge segments within passage context wu et al. (2021) . in this work, we propose to capture both intra-and inter-document relationships of the knowledge segments (figure 2 (c)) in the grounding documents to guide the smooth and natural knowledge selection and transitions for document-grounded conversations. however, how to apply such a static knowledge graph to dialog flow management has always been a problem. many previous studies @xcite have used graph structures to constrain search (e.g. confining the next topic to neighboring areas), but have also ignored deeper integration of dialog contexts and knowledge graphs, such as optimal knowledge representation to capture dialogue flow information. based on the considerations above, we propose to first capture the inter-and intra-document knowledge relationships as a heterogeneous document graph, and then exploit the graph effectively for dialog flow management through fine-to-coarse contextualization -from the local word-level knowledge attentions, to knowledge interactions in document graphs, and finally to the knowledge transition flow along dialogue turns. specifically, we design a two-level document graph consisting of topic (i.e. document) and knowledge vertices connected by inter-and intra-document relations (figure 1 ). the topic vertices correspond one-to-one to the grounding documents, while the knowledge vertices refer to the knowledge segments from each document. the knowledge vertices are connected to the corresponding topic vertices they belong to. meanwhile, the graph connects the knowledge segments within the same document by their co-referential mentions, and the documents are connected based on similarity or commonsense relationships. hence we call the graph @xcite . we then propose our corefdiffs method which leverages coref-mdg's graph structure and integrates dialog flow for knowledge contextualization and selection. corefdiffs focuses on the inter-turn knowledge difference flow in the dialog histories by means of a novel differential linearization module. our contributions in this paper can be summarized as follows. 1) we develop coref-mdg, a novel multi-document graph structure incorporating co-referential mentions. when leveraged in guiding document-grounded conversations in our corefdiffs methodology, it empirically outperformed alternative graph structures; 2) we systematically study the different kinds of inter-and intradocument relations and show that document-level semantics, such as co-reference and sentence order, are significant factors for knowledge selection (sec. 4.4); 3) our corefdiffs achieves state-of-the-art on wow, holl-e, multidoc2dial and cmu-dog datasets, for both knowledge selection and response generation tasks. dialogue history 𝑢 𝑡-1 : blue is always nice. i like royal blue. 𝑟 𝑡-1 : i once rode on the royal blue train from new york to d.c 𝑢 𝑡 : oh that sounds really nice. i bet there was a lot of scenery and blue skies. 𝑟 𝑡 : yes, speaking of blue skies, have you seen the 1946 movie staring bing crosby? blue royal blue (train) blue skies (1946 film) 3 x 2 1 words overlap, sub-class transformer transformer transformer … … … words overlap words overlap diff diff diff x yes/no graph propagation diff sequence target node x 1 x 2 x 3 𝒖 𝒕 𝒓 𝒕-𝟏 … 𝒃𝒍𝒖𝒆𝒌 𝟏 𝒌 𝟐 𝒌 𝟑 𝒖 𝒕 𝒓 𝒕-𝟏 … 𝐛𝐥𝐮𝐞 𝐬𝐤𝐢𝐞𝐬𝒌 𝟒 𝒌 𝟓 𝒖 𝒕 𝒓 𝒕-𝟏 … 𝐫𝐨𝐲𝐚𝐥 𝐛𝐥𝐮𝐞𝒌 𝟔 𝒌 𝟕 𝒌 𝟖 iii. linearization i. local association x ii. structural propagation figure 3: corefdiffs architecture. green round rectangles and blue circles are topic and knowledge vertices, respectively. steps i to iii contextualize knowledge in an increasingly fine-grained manner: first, i) by vertex embedding by bert; then, ii) by propagating coref-mdg information, and finally iii) by linearizing the knowledge representations, according to the dialog's historical knowledge sequence. knowledge graph for conversations. knowledge graphs were also often used in dialog management, such as dialog transition graphs @xcite constructed from common transitions present in a dialog corpus and off-the-shelf commonsense graphs @xcite . there were also some works @xcite transforming unstructured text into structures or combining triplets and texts into graphs. for example, @xcite constructed key phrases into graphs according to their order in stories. interestingly, to the best of our knowledge, co-reference mentions have not been considered in such document graph construction although it has been proved critical in learning language models for reasoning intensive nlp tasks @xcite . to apply knowledge graphs for dialog, many existing works @xcite used the graph structures to confine the search space and optimized selection through hand-crafted rewards. in contrast, we incorporate the knowledge graph into dialog management by learning knowledge representations from the graph structure. sequence learning in dialog. sequence learning is essential for conversations. several studies @xcite explored the historical knowledge sequence to select knowledge for document-grounded dialog. for example, @xcite captured knowledge sequence by a latent variable, while @xcite ) further proposed to learn abstract topic sequence to mitigate the issues of knowledge sparsity and knowl-edge transition noise. inspired by the importance of exploiting knowledge difference @xcite ) for informative dialog, we extend the use of dialog knowledge differences into sequences, thus capturing the knowledge shift patterns from turns with longer distances as well as the sequential patterns of knowledge transitions in a dialogue. ## approach figure 3 shows the overall architecture of our approach. as shown in the dialog history part, in each data sample, given a dialog history @xmath0, r t-l , ..., r t-1 , u t } of l turns and a set of grounding documents @xmath1, .., d i , ..., d |d| }, where u * and r * are utterances from the user and chatbot, respectively. ## experiments datasets. we validate our method on four public benchmarks for document-grounded conversation, wow @xcite , holl-e @xcite , cmu-dog @xcite and multi-doc2dial @xcite . the dataset statistics are summarized in evaluation metrics. we focused on evaluating the knowledge selection sub-task for the documentgrounded dialog system, based on the knowledge and topic selection accuracies, denoted as kl and tp, respectively. we also explore the knowledge selection accuracy of all intra-topic data samples, whose knowledge transitions are within the same topic, denoted as in-tp. as for evaluating the subtask of response generation given the dialog context and selected knowledge, we calculate the overlap of the generated response and the ground-truth with the unigram-f1(uf1) and bigram-f1(bf1). baselines. for the two commonly used datasets, wow and holl-e, we split the baselines into three categories based on their text encoder types. (i) non-pretrained encoder: trans-former+memnet @xcite is the baseline released with the dataset wow. dif-fks(rnn) @xcite incorporates the knowledge difference feature in knowledge selection. (ii) bert encoder: bert+poks, a variant of poks with bert @xcite encoder, learns knowledge selection by posterior knowledge distribution. slks @xcite captures historical knowledge sequence with a latent variable. pipm @xcite improves slks by addressing the problem of missing posterior distribution in test phase. colv @xcite includes two collaborative variables for knowledge selection and response generation. knowl-edgpt @xcite optimizes knowledge grounded dialog task by the pre-trained bert encode and gpt-2 @xcite . (iii) passage-level bert encoder: dialki @xcite encodes knowledge at passage level to capture knowledge segment relations as we do in corefdiffs. for response generation, given that the above-mentioned methods adopted different generators, we uniformly replaced their generators with a prompt-based generator prefixtuning @xcite for a fair comparison, thus forming the baselines with "*" in ## conclusion we show the significance of utilizing the document's semantic structures and relations for managing dialog flow. we embody these relations in our novel multi-document graph coref-mdg which models co-referential knowledge mention links and inter-document relations. our analysis of coref-mdg yields insights of how the difference among intra-or inter-document relations affect the final topic and knowledge selection accuracy. for example, we find that coreference links and topic-knowledge sentence order relations are critical relations. we then build dynamicallysensitive dialog flows via our corefdiffs method, which integrates the modeling of dialog difference flow with the prior knowledge represented in coref-mdg. corefdiffs demonstrates that it is possible to seamlessly integrate static graph structures with dynamic dialog-specific flows, improving documentgrounded conversations.