Computer Science and Engineering Department at Seoul National University
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Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
This work collects Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit, and proposes a novel abstractive summarization model named multi-level memory networks (MMN), equipped with multi- level memory to store the information of text from different levels of abstraction.
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
This work shows that Curiosity-Bottleneck learns an effective exploration strategy by robustly measuring the state novelty in distractive environments where state-of-the-art exploration methods often degenerate.
KLUE: Korean Language Understanding Evaluation
- Sungjoon Park, Jihyung Moon, Kyunghyun Cho
- Computer ScienceNeurIPS Datasets and Benchmarks
- 20 May 2021
KLUE is a collection of 8 Korean natural language understanding tasks, including Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking, and a comprehensive documentation on creating KLUE will facilitate creating similar resources for other languages in the future.
Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
Taking inspiration from social cognition, a generative estimator is used to infer emotion cause words from utterances with no word-level label and a novel method based on pragmatics is introduced to make dialogue models focus on targeted words in the input during generation.
How Robust are Fact Checking Systems on Colloquial Claims?
It is found that existing fact checking systems that perform well on claims in formal style significantly degenerate on colloquial claims with the same semantics, and it is shown that document retrieval is the weakest spot in the system even vulnerable to filler words, such as “yeah” and “you know”.
Will I Sound like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness
Inspired by social cognition and pragmatics, existing dialogue agents are endow with public self-consciousness on the fly through an imaginary listener to enforce dialogue agents to refrain from uttering contradiction and improve consistency of existing dialogue models.
Public Self-consciousness for Endowing Dialogue Agents with Consistent Persona
This approach, based on the Rational Speech Acts framework, attempts to maintain consistency in an unsupervised manner requiring neither additional annotations nor pretrained external models to improve consistency in dialogue agents.