Existing English-based text similarity measurements primarily focus on the semantic dimension, neglecting the unique linguistic attributes found in languages like Korean, where honorific expressions are explicitly integrated. To address this limitation, this study proposes Kosmic, a novel Korean text-similarity metric that encompasses the semantic and tonal facets of a given text pair. For the evaluation, we introduce a novel benchmark annotated by human experts, empirically showing that Kosmic outperforms the existing method. Moreover, by leveraging Kosmic, we assess various Korean paraphrasing methods to determine which techniques are most effective in preserving semantics and tone.

Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability of training datasets.To address this issue, we propose Multi-Passage to Dialogue (MP2D), a data generation framework that automatically creates conversational questionanswering datasets with natural topic transitions.By leveraging the relationships between entities in a knowledge graph, MP2D maps the flow of topics within a dialogue, effectively mirroring the dynamics of human conversation.It retrieves relevant passages corresponding to the topics and transforms them into dialogues through the passage-to-dialogue method.Through quantitative and qualitative experiments, we demonstrate MP2D's efficacy in generating dialogue with natural topic shifts.Furthermore, this study introduces a novel benchmark for topic shift dialogues, TS-WikiDialog.Utilizing the dataset, we demonstrate that even Large Language Models (LLMs) struggle to handle topic shifts in dialogue effectively, and we showcase the performance improvements of models trained on datasets generated by MP2D across diverse topic shift dialogue tasks.

To address the data scarcity issue in Conversational question answering (ConvQA), a dialog inpainting method, which utilizes documents to generate ConvQA datasets, has been proposed.However, the original dialog inpainting model is trained solely on the dialog reconstruction task, resulting in the generation of questions with low contextual relevance due to insufficient learning of question-answer alignment.To overcome this limitation, we propose a novel framework called Dialogizer, which has the capability to automatically generate ConvQA datasets with high contextual relevance from textual sources.The framework incorporates two training tasks: question-answer matching (QAM) and topic-aware dialog generation (TDG).Moreover, re-ranking is conducted during the inference phase based on the contextual relevance of the generated questions.Using our framework, we produce four Con-vQA datasets by utilizing documents from multiple domains as the primary source.Through automatic evaluation using diverse metrics, as well as human evaluation, we validate that our proposed framework exhibits the ability to generate datasets of higher quality compared to the baseline dialog inpainting model.

We built a personalized example-based dialog system that constructs its responses by considering entities that the user has uttered, and topics in which the user has expressed interest. The system analyzes user input utterances, then uses DBpedia and Freebase to extract relevant entities and topics. The extracted entities and topics are stored in personal knowledge memory and are used when the system selects responses from the example database and generates responses. We conducted a human experiment in which evaluators rated dialog systems based on subjective metrics. The proposed dialog system that uses topics that are of interest to the user achieved higher evaluation scores for both personalization and satisfaction than the baseline systems. These results demonstrate that the use of topics in the system response provides a sense that the system pays attention to the user's utterances; as a consequence the user has a satisfactory dialog experience.

We developed a natural language dialog listening agent that uses a knowledge base (KB) to generate rich and relevant responses. Our system extracts an important named entity from a user utterance, then scans the KB to extract contents related to this entity. The system can generate diverse and relevant responses by assembling the related KB contents into appropriate sentences. Fifteen students tested our system; they gave it higher approval scores than they gave other systems. These results demonstrate that our system generated various responses and encouraged users to continue talking.

In spoken English, vowels in non-stressed syllables are often reduced to a brief neutral vowel (e.g, e or ι). Non-native speakers of English may not use this `vowel reduction' correctly, so their utterances may sound unnatural. We propose an automatic system to provide feedback about vowel-reduction to non-native speakers of English. The system has three parts: it predicts vowel reduction, detects vowel reduction in speech, compares the prediction to the detected sound to generate a score then uses this score to provide corrective feedback to the speaker. The system had good accuracy and provided positive learning results for the user. The proposed system can be used as a part of a computer-assisted language learning system.

This article presents an approach to nonnative pronunciation variants modeling and prediction. The pronunciation variants prediction method was developed by generalized transformation-based error-driven learning (GTBL). The modified goodness of pronunciation (GOP) score was applied to effective mispronunciation detection using logistic regression machine learning under the pronunciation variants prediction. English-read speech data uttered by Korean-speaking learners of English were collected, then pronunciation variation knowledge was extracted from the differences between the canonical phonemes and the actual phonemes of the speech data. With this knowledge, an error-driven learning approach was designed that automatically learns phoneme variation rules from phoneme-level transcriptions. The learned rules generate an extended recognition network to detect mispronunciations. Three different mispronunciation detection methods were tested including our logistic regression machine learning method with modified GOP scores and mispronunciation preference features; all three methods yielded significant improvement in predictions of pronunciation variants, and our logistic regression method showed the best performance.