Papers by Byeongchang Kim

5 papers
Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness (2020.emnlp-main)

Copied to clipboard

Challenge: Existing models for improving consistency often train with additional NLI labels or attach trained extra modules to the generative agent.
Approach: They propose to encode personas into dialogue embeddings and a persona-conditioned dialogue dataset to improve persona consistency.
Outcome: The proposed approach can enforce dialogue agents to refrain from contradictions and improve consistency of existing models.
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks (N19-1)

Copied to clipboard

Challenge: Abstractive summarization methods suffer from inferior performance compared to extractive methods.
Approach: They propose a reddit TIFU dataset and a new abstractive summarization model . they use multi-level memory networks to store information from different levels of abstraction .
Outcome: The proposed model outperforms state-of-the-art summarization models with multi-level memory . the proposed dataset is highly abstractive and outperformed existing models with the proposed model .
Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes (2021.emnlp-main)

Copied to clipboard

Challenge: Empathy is a complex cognitive ability based on the reasoning of others’ affective states.
Approach: They propose a method to infer emotion cause words from utterances without a word-level label and a novel method to make dialogue models focus on targeted words in the input during generation.
Outcome: The proposed method improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of automatic and human evaluation.
How Robust are Fact Checking Systems on Colloquial Claims? (2021.naacl-main)

Copied to clipboard

Challenge: Existing fact checking systems that perform well on colloquial claims significantly degenerate on collotic claims with the same semantics.
Approach: They propose to transfer the styles of claims from FEVER into colloquialism to investigate fact checking systems on colloqual claims.
Outcome: The proposed system significantly degenerates on colloquial claims with the same semantics.
AudioCaps: Generating Captions for Audios in The Wild (N19-1)

Copied to clipboard

Challenge: a dataset of 46K audio clips with human-written text pairs is used to generate captions for audio . the task of translating a multimedia input source into natural language has been extensively studied over the past few years .
Approach: They propose a top-down multi-scale encoder and aligned semantic attention for audio captioning.
Outcome: The proposed captions are faithful to audio inputs and better than existing models.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations