Naturalness of an Utterance Based on the Automatically Retrieved Commonsense

In this research we investigated user’s behavior while facing a system coping with common knowledge about keywords and compared it with not only classic word-spotting method but also with random text-mining. We show how even a simple implementation of our idea can enrich the conversation and increase the naturalness of computer’s utterances. Our results show that even very commonsensical utterances are more natural than classic approaches and also methods we developed to make a conversation more interesting. For arousing opinion exchange during the session, we will also briefly introduce our idea of combining latest NLP achievements into one holistic system where the main engine we want to base on commonsense processing and affective computing.

Support for Internet-Based Commonsense Processing – Causal Knowledge Discovery Using Japanese “If” Forms

Abstract. This paper introduces our method for causal knowledge re-trieval from the Internet resources, its results and evaluation of using it in utterance creation process. Our system automatically retrieves common-sensical knowledge from the Web resources by using simple web-mining and information extraction techniques. For retrieving causal knowledge the system uses three of specific several Japanese “if ” forms. From the results we can conclude that Japanese web pages indexed by a common search engine spiders are enough to discover common causal relationships and this knowledge can be used for making Human-Computer Interfaces sound more natural and interesting than while using classic methods

Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents

Human-like chatbots necessitate the use of commonsense reasoning in order to effectively comprehend and respond to implicit information present within conversations. Achieving such coherence and informativeness in responses, however, is a non-trivial task. Even for large language models (LLMs), the task of identifying and aggregating key evidence within a single hop presents a substantial challenge. This complexity arises because such evidence is scattered across multiple turns in a conversation, thus necessitating integration over multiple hops. Hence, our focus is to facilitate such multi-hop reasoning over a dialogue context, namely dialogue chain-of-thought (CoT) reasoning. To this end, we propose a knowledge distillation framework that leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters. We further present DOCTOR, a DialOgue Chain-of-ThOught Reasoner that provides reliable CoT rationales for response generation. We conduct extensive experiments to show that enhancing dialogue agents with high-quality rationales from DOCTOR significantly improves the quality of their responses.Comment: 25 pages, 8 figures, Accepted to EMNLP 202

Robust Dialog Management Through A Context-centric Architecture

This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user’s goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine’s ability to communicate may be hindered by poor reception of utterances, caused by a user’s inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by ContextBased Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user’s assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users

The effects of pausing on comprehensibility

Pausing effects how well listeners understand and attend to meaning in discourse. This study investigates the effects of three different pause treatments (irregular placement, increased frequency, and longer length) on comprehensibility ratings. Varonis and Gass (1982) found that a complex interaction of factors affected comprehensibility ratings. These included pronunciation, grammar, familiarity and fluency. While many of these features have been investigated (Anderson-Hsieh, Johnson, & Koehler, 1992; Derwing & Munro, 1997; Hahn, 2004; Isaacs & Trofimovich, 2012; Kang, 2010; Munro & Derwing, 1995), pausing has received little attention. In this study, an extended NNS speech sample with native-like pausing was manipulated, creating three experimental recordings, one with irregularly placed pauses, one with increased pause frequency, and one with longer pauses. Forty-three undergraduates in four different class groups listened to each of the pause treatments and rated them for comprehensibility. In addition to comprehensibility measures, participants also rated each treatment for fluency based on Griffiths\u27 (1991) proposition that pausing is often tied to fluency. This allowed for a comparison of the effects of pausing on comprehensibility to those on fluency. Additionally, this study investigated the strength of Kang\u27s (2010) revised comprehensibility instrument. The results showed that irregular pause placement was the greatest detriment to comprehensibility, followed by pause frequency. These results may be explained by the psycholinguistic model of language processing which assumes we process language in chunks. When NNSs pause irregularly, NS listeners must process each word individually to make meaning instead of processing the chunk through expected phrasing. This, in turn, causes lower comprehensibility ratings. These results advocate for the teaching of formulaic language in the ESL/EFL classroom so that attention to pause placement in conjunction with work on fluency facilitates more comprehensible speech

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations