2022
pdf
abs
FreeTalky: Don’t Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue
Chanjun Park
|
Yoonna Jang
|
Seolhwa Lee
|
Sungjin Park
|
Heuiseok Lim
Proceedings of the Thirteenth Language Resources and Evaluation Conference
We propose a deep learning-based foreign language learning platform, named FreeTalky, for people who experience anxiety dealing with foreign languages, by employing a humanoid robot NAO and various deep learning models. A persona-based dialogue system that is embedded in NAO provides an interesting and consistent multi-turn dialogue for users. Also, an grammar error correction system promotes improvement in grammar skills of the users. Thus, our system enables personalized learning based on persona dialogue and facilitates grammar learning of a user using grammar error feedback. Furthermore, we verified whether FreeTalky provides practical help in alleviating xenoglossophobia by replacing the real human in the conversation with a NAO robot, through human evaluation.
pdf
abs
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation
Jaehyung Seo
|
Seounghoon Lee
|
Chanjun Park
|
Yoonna Jang
|
Hyeonseok Moon
|
Sugyeong Eo
|
Seonmin Koo
|
Heuiseok Lim
Findings of the Association for Computational Linguistics: NAACL 2022
Recent natural language understanding (NLU) research on the Korean language has been vigorously maturing with the advancements of pretrained language models and datasets. However, Korean pretrained language models still struggle to generate a short sentence with a given condition based on compositionality and commonsense reasoning (i.e., generative commonsense reasoning). The two major challenges are inadequate data resources to develop generative commonsense reasoning regarding Korean linguistic features and to evaluate language models which are necessary for natural language generation (NLG). To solve these problems, we propose a text-generation dataset for Korean generative commonsense reasoning and language model evaluation. In this work, a semi-automatic dataset construction approach filters out contents inexplicable to commonsense, ascertains quality, and reduces the cost of building the dataset. We also present an in-depth analysis of the generation results of language models with various evaluation metrics along with human-annotated scores. The whole dataset is publicly available at (https://aihub.or.kr/opendata/korea-university).
pdf
abs
You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge
Jungwoo Lim
|
Myunghoon Kang
|
Yuna Hur
|
Seung Won Jeong
|
Jinsung Kim
|
Yoonna Jang
|
Dongyub Lee
|
Hyesung Ji
|
DongHoon Shin
|
Seungryong Kim
|
Heuiseok Lim
Findings of the Association for Computational Linguistics: EMNLP 2022
To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model that considers knowledge and persona at the same time is still limited, leading to hallucination and a passive way of using personas. We propose an effective dialogue agent that grounds external knowledge and persona simultaneously. The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder. Then, our model generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. We conduct experiments on the persona-knowledge chat and achieve state-of-the-art performance in grounding and generation tasks on the automatic metrics. Moreover, we validate the answers from the models regarding hallucination and engagingness through human evaluation and qualitative results. We show our retriever’s effectiveness in extracting relevant documents compared to the other previous retrievers, along with the comparison of multiple candidate scoring methods. Code is available at
https://github.com/dlawjddn803/INFOpdf
abs
PicTalky: Augmentative and Alternative Communication for Language Developmental Disabilities
Chanjun Park
|
Yoonna Jang
|
Seolhwa Lee
|
Jaehyung Seo
|
Kisu Yang
|
Heuiseok Lim
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations
Children with language disabilities face communication difficulties in daily life. They are often deprived of the opportunity to participate in social activities due to their difficulty in understanding or using natural language. In this regard, Augmentative and Alternative Communication (AAC) can be a practical means of communication for children with language disabilities. In this study, we propose PicTalky, which is an AI-based AAC system that helps children with language developmental disabilities to improve their communication skills and language comprehension abilities. PicTalky can process both text and pictograms more accurately by connecting a series of neural-based NLP modules. Additionally, we perform quantitative and qualitative analyses on the modules of PicTalky. By using this service, it is expected that those suffering from language problems will be able to express their intentions or desires more easily and improve their quality of life. We have made the models freely available alongside a demonstration of the web interface. Furthermore, we implemented robotics AAC for the first time by applying PicTalky to the NAO robot.
pdf
bib
Proceedings of the 1st Workshop on Customized Chat Grounding Persona and Knowledge
Heuiseok Lim
|
Seungryong Kim
|
Yeonsoo Lee
|
Steve Lin
|
Paul Hongsuck Seo
|
Yumin Suh
|
Yoonna Jang
|
Jungwoo Lim
|
Yuna Hur
|
Suhyune Son
Proceedings of the 1st Workshop on Customized Chat Grounding Persona and Knowledge
2020
pdf
abs
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning
Jungwoo Lim
|
Dongsuk Oh
|
Yoonna Jang
|
Kisu Yang
|
Heuiseok Lim
Proceedings of the 28th International Conference on Computational Linguistics
CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance with distributed representation without considering the process of predicting the answer from the semantic representation of the question. To shed light upon the semantic interpretation of the question, we propose an AMR-ConceptNet-Pruned (ACP) graph. The ACP graph is pruned from a full integrated graph encompassing Abstract Meaning Representation (AMR) graph generated from input questions and an external commonsense knowledge graph, ConceptNet (CN). Then the ACP graph is exploited to interpret the reasoning path as well as to predict the correct answer on the CommonsenseQA task. This paper presents the manner in which the commonsense reasoning process can be interpreted with the relations and concepts provided by the ACP graph. Moreover, ACP-based models are shown to outperform the baselines.