Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk (2022)

Abstract: The capability of holding social talk (or casual conversation) and making sense of conversational content requires context-sensitive natural language understanding and reasoning, which cannot be handled efficiently by the current popular open-domain dialog systems and chatbots. Heavily relying on corpus-based machine learning techniques to encode and decode context-sensitive meanings, these systems focus on fitting a particular training dataset, but not tracking what is actually happening in a conversation, and therefore easily derail in a new context. This work sketches out a more linguistically-informed architecture to handle social talk in English, in which corpus-based methods form the backbone of the relatively context-insensitive components (e.g. part-of-speech tagging, approximation of lexical meaning and constituent chunking), while symbolic modeling is used for reasoning out the context-sensitive components, which do not have any consistent mapping to linguistic forms. All components are fitted into a Bayesian game-theoretic model to address the interactive and rational aspects of conversation.

## introduction and background developing dialog systems that can socially communicate with humans and make sense of conversational content would demonstrate that we are able to put together all linguistic knowledge and skills in action in a truly personalized manner, i.e. the dialog systems can use the same language competence to produce different coherent contents in different conversational situations. such domainindependence would allow a dialog system to be robustly used across multiple content and task domains. while the benefit of using social interaction style in real-life dialog systems is a controversial topic (e.g. @xcite , the availability of social dialog agents can alleviate the critical shortage of human resources, e.g. in language education @xcite , among other domains. worldwide language learners often have little time to develop their communication skills with their teachers in the classroom setting; consequently teachers do not have enough clues to analyze their students' communication competence and their own teaching effectiveness. having a dialog system that can socially converse with students at least about the learning materials outside the classroom and being able to access the content of these conversations for further analysis would be a plausible solution to the bottleneck of human resources in language education. research on social dialog systems has thrived in recent years thanks to optimism about the robustness of natural language processing (nlp) techniques based on big data and deep learning. within this period, emerging dialog systems either (1) seek to accommodate the open-domain information exchange by expanding the list of predefined interlocutors' intents and conversational topics for modular architectures, e.g. systems competing in alexa prize socialbot challenges @xcite , or (2) focus on generating utterances to prolong the conversation based on neural network sequenceto-sequence approaches, such as google's meena @xcite ) and meta's blenderbot @xcite . primarily motivated by the available data and statistical techniques but not the sociocultural characteristics of social talk, these dialog systems focus on fitting pre-existing training datasets, but not tracking what is actually happening in a conversation. as a consequence, they easily derail in a new context, as illustrated in figure 1 : the chatbot seems to interpret bank in the question what do you do at a river bank? as a financial institution, which is statistically preferred given the training data, instead of the land alongside a river, which is more felicitous in this particular context. to address that shortcoming, this work takes a novel approach, relying on in-depth analysis of naturally occurring data to sketch out a more linguistically-informed foot_1 architecture to handle social talk in english, in which corpus-based methods form the backbone of the relatively contextinsensitive components (e.g. part-of-speech (pos) tagging, approximation of lexical meaning, and constituent chunking), while symbolic modeling is used for reasoning out the context-sensitive components, which do not have any consistent mapping to linguistic forms. all components are fitted into a bayesian game-theoretic model to address the interactive and rational aspects of conversation. this paper is organized as follows. section 2 provides a working definition of interlocutors' shared goal in social talk and its implications. section 3 presents the key aspects of coherence with respect to the shared goal foot_2 . section 4 outlines the conversational context that accounts for the coherence-based and goal-directed nature of social talk. section 5 describes a simple worked example of the proposed model. section 6 discusses the key differences between the proposed approach and current popular approaches to social dialog systems, analyzing its advantages and limitations, research priorities, and ethics and social impact considerations. section 7 concludes and presents a plan for future work. following the literature on intersubjectivity in communication (e.g. @xcite @xcite , i propose that the shared goal of interlocutors in social talk is to create a coherent experience of together making sense of self, the other, and the relationship between them (but not necessarily to share the same perspective on any aspect of the conversational content). this shared goal is not only primarily addressed by social talk but also forms a part of natural task-oriented conversation when the interlocutors attempt to build mutual rapport. even the task-related conversational goals can be considered instantiations of this shared goal when self and the other are playing specific social roles in the task domains, e.g. seller -buyer or consultant -client. within this shared goal, performing a conversational move implies taking a stance, i.e. a public social act of simultaneously evaluating objects (directly or indirectly) discussed in the conversational move, positioning subjects (self or the other or both), and aligning with the other subject, with respect to any salient dimension of the sociocultural field @xcite . regarding the sociocultural field, i adopt the proposal in @xcite and @xcite for representing conversational interactions between self and the other as falling into one of three dimensions: epistemic (knowledge/information exchange -how knowledgeable the interlocutors are), normative foot_3 (power/social distance -how powerful the interlocutors are), and affective (affect/emotion -how emotional the interlocutors are). by explicitly paying attention to the normative and affective domains in the conversational context, we can expand current models of dialog that involve a representation of context but focus on the epistemic domain (such as those that build on @xcite @xcite , and therefore can adequately handle the multifaceted coherence in social talk and the corresponding social reasoning. where there is at least one content-based coherence relation between the target utterance and prior discourse, this relation is shaped by certain discourse hooks foot_4 (located in the target utterance) that are pragmatically accessible to the hearer, and can be discourse-old, discourse-new bearing inferential relation to discourse-old, or discourse-new and related to discourse-old in a non-inferential manner (cf. @xcite . for example, in the social dialog in when there is no content-based coherence relation between the target utterance and prior discourse, conversational coherence is demonstrated by the shift of social focus created by certain explicit positioning or alignment signals in the target utterance. for example, the utterance 147-a of the excerpt shown in utt. simplified transcript 131-a well rosemary and i went in for a look and uhm i found a lovely red dress 132-a and i was like delighted with it and everything 133-a and i brought mum up to see it and she totally ditched it 134-b yeah 135-b yeah 136-b why 137-a she said it looked like she was she was saying it didn't do anything for my hips 138-a it made my hips look big and like you know my bum and hips and everything 139-a i was really excited cos i had the dress and then i just 140-b but did you like it 141-a yeah 142-b but she turned you off it 143-a yeah well i mean i'm hardly going to wear it now seeing everyone thinking i've big hip 144-a hip girl 145-a i'll be called hippy 146-a hippo 147-a ah so how are you anyway coherence and relevance it is worth noting that to be coherent, an utterance must not only be connected to the prior discourse via certain inferences, but also be relevant to the conversational goals, which coordinate the sequences of action performed by interlocutors' utterances (cf. clift, 2016, pp. 89-94 for the discussion on coherence in interaction). within the shared goal of interlocutors in social talk defined in section 2, the expression of this relevance varies according to sociocultural dimensions. to be epistemically relevant, an utterance must, at least, introduce a new focus of discussion instead of simply repeating the old information. to be affectively relevant, an utterance can mimic the emotional intensity or support the sentiment of the immediately preceding discourse. for example, the interlocutors clearly show their matching emotional intensity in the excerpt of a face-to-face social dialog in coherence and consistency another aspect of conversational coherence is the consistency of the interlocutors' conversational contents and psychological behaviors, which are subsumed in the term 'speaker type' in this paper. an utterance is incoherent if it commits an object evaluation, e.g. i like cats, that conflicts with another evaluation of the same object by the same speaker in prior discourse, e.g. i hate cats. one of the popular attempts to address this problem is the creation of the persona-chat dataset for training and testing the aspects of persona consistency in chatbot models @xcite ). an utterance is also less coherent if it demonstrates some dramatic change in its speaker's behaviors, e.g. a rude statement from a speaker who is very polite in prior discourse. from the production perspective, a speaker would like to maintain their behavioral consistency; while from the interpretation perspective, a hearer would assume this consistency from the speaker to effectively decode the meaning of the speaker's utterance. previous work such as fang et al. @xcite shows that understanding the speaker's personality in the dialog helps the hearer in having better interaction strategies. it's worth noting that interlocutors' psychological behaviors vary according to different factors of the speech situation such as the cultural conditions, the interlocutors' personalities, and the relationship between interlocutors. for example, the social distance between interlocutors can affect the course and topics of discussion. comparing the dialog in ## context representation and update to be capable of reasoning about multifaceted coherence in social talk presented in section 3, a linguistically-driven dialog model needs an adequate representation of the conversational context that consists of essential linguistic information obtained from either neural or symbolic knowledge. to optimally exploit both sources of knowledge, the relatively context-insensitive components of the conversational context are deduced by machine learning techniques; while the more contextsensitive components, which do not have any consistent mapping to linguistic forms, are reasoned out by symbolic methods. this division of labor takes advantage of the knowledge of pretrained statistical models as prior experiences to approximate linguistic meanings, at the same time separate them from the real-time meanings co-constructed by interlocutors in a specific conversational context via symbolic reasoning. to facilitate the reasoning, the conversational context has direct access to knowledge sources including linguistic dictionaries and thesauri, and world knowledge bases. an all-in-one option for knowledge sources is wolfram engine. specifically, using statistical models of off-theshelf nlp libraries such as spacy, we can automatically obtain basic linguistic annotations of an utterance including word tokens, their pos tags and contextual embeddings, syntactic relations between word tokens (as the result of dependency parsing), and linguistic constituents (including named entities). based on these pieces of linguistic information, the discourse hooks in an utterance are identified by various heuristics such as: relying on linguistic definitions and relations: further, to represent non-epistemic dimensions, it is necessary to annotate at least the following: affective: intances of highly expressive language in the target utterance such as adjectives and idioms (which can be identified by analyzing their definition and properties recorded in the linguistic dictionaries) and their sentiments (which can retrieved from off-the-shelf sentiment analysis models) normative: default and emphasized positioning and alignment in the target utterance which can be identified based on the clause type of the utterance and the absence or presence of self and the other in its linguistic content; for example: ## a worked example to demonstrate how the proposed dialog model works, a proof-of-concept text-based dialog system was developed based on the free wolfram engine for developers and spacy v2.3.5 11 , including its small core model for english and the dis-tilbert model @xcite , accessed via spacy-transformers v0.6.x 11 . figure 2 shows the conversational context created by the system after the human interlocutor enters the text string i just had a walk along the connecticut river. the bank is gorgeous. (please refer to appendix a for the snapshots of step-by-step context update). the system uses spacy's core model to tokenize the text string into word tokens (including punctuation marks), provide their pos tags, and then segment the sequence of tokens into sentences with their dependency structures. the system relies on distilbert to obtain the contextual embeddings of tokens and sequences of tokens so that it can calculate similarity scores between these embeddings to reason out the most appropriate senses of semantically ambiguous words as well as the potential discourse hook relations between linguistic constituents. for each open class word, i.e. an adjective, adverb, interjection, noun or verb, the system first retrieves all of wordnet senses from wolfram knowledge base (via wolfram engine), and then identifies the real-time context-sensitive sense, as shown under each of these words in figure 2 . each real-time sense is the one whose contextual embedding (calculated based on its textual definition) is the most similar to the contextual embedding of the corresponding word. they not only represent the human-readable meanings, but also participate in the creation of future discourse hook relations. next, the system adds a meta-data token storing speaker information to each sentence before 11 under the mit license. based on the pronoun i, the punctuation "." and the dependency links, the system recognizes that the first sentence is a declarative which has self as the subject. consequently, this sentence features the emphasized self positioning. moving to the second sentence, the system first examines alternative discourse hook relations between its sole noun phrase and other noun phrases in prior discourse, which results in the selection of the most salient relation between the bank and the connecticut river, corresponding to the highest similarity score (0.892) between the embeddings of the realtime senses of the head nouns bank and river. the system then marks the adjective gorgeous as an instance of highly expressive language because its definition contains a degree adverb (dazzlingly). to produce the most relevant response, the system puts more weight on the candidates addressing the second sentence as it is more recent. the highest salience score is achieved when the response includes both the bank and the emotional resonance of gorgeous, an instance of positive sentiment. consequently, the system adds positive elements such as the predicate like to its planned response. a possible template for this planned response is "it seems that you like ... a lot, right?", which results in the ultimate response as it seems that you like the bank a lot, right? foot_10 ## discussion departing from current popular approaches to social dialog systems, which rely on available mod-els for similar tasks foot_11 and conversational data created in artificial or asynchronous settings @xcite , this work starts with empirical analysis of naturally occurring data, i.e. human-human casual conversation in real life, to systematically define key linguistic characteristics of social dialog which can be modeled based on sota nlp techniques. this approach is in line with the preregistration practice promoted by @xcite , entailing both advantages and limitations. by specifying what i want to capture in my model before the actual implementation, i can avoid the post-hoc problems faced by heavily data-driven architectures (e.g. @xcite . however, not relying on benchmark data and their corresponding techniques, i can not prove the practicality and reproducibility of my model in an actionable way before a full-blown dialog system is implemented. in addition, while this work starts with human-human conversation, its ultimate outcome is human-computer conversation which definitely diverges from the input guiding data and can potentially direct the research agenda into unplanned territories. it is also worth noting that while the modularity of the proposed architecture allows independent and simultaneous improvements of its components, its effectiveness can suffer from cumulative parsing errors caused by its pipeline design. moreover, the statistical models of off-the-shelf nlp libraries used in the proposed architecture, mostly trained on planned text (e.g. @xcite , may not work well on spontaneous conversation. ## conclusion and future work this paper sketches out a novel dialog model for social conversation in english, motivated by a thorough investigation of the nature and linguistic characteristics of the phenomenon, including the shared goal between interlocutors and multifaceted coherence across different sociocultural dimensions. next, i will implement a full-blown dialog system based on this model and develop adequate evaluation protocols, before iteratively evaluating and improving the system until it can consistently hold casual conversations with humans. subsequently, i will use these conversations and their contextual representation as a new window into the social interaction between humans and reasoning machines. figure 13 : calculate and rank similarity scores between bank and each of its dictionary sense definitions. before that, the contextual embedding of bank was recalculated based on a modified version of the second sentence, which is the connecticut river, the bank is gorgeous. this enhancement of the real-time context-sensitive meaning of bank is informed by the fact that the connecticut river is the noun phrase in the first sentence whose head noun, i.e. river, is the closest to bank in terms of similarity scores between their contextual embeddings.