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Found 8 papers, 4 papers with code
Revealing User Familiarity Bias in Task-Oriented Dialogue via Interactive Evaluation
Most task-oriented dialogue (TOD) benchmarks assume users that know exactly how to use the system by constraining the user behaviors within the system's capabilities via strict user goals, namely "user familiarity" bias.
Aligning Large Language Models through Synthetic Feedback
In this work, we propose a novel framework for alignment learning with almost no human labor and no dependency on pre-aligned LLMs.
Keep Me Updated! Memory Management in Long-term Conversations
Remembering important information from the past and continuing to talk about it in the present are crucial in long-term conversations.
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models
In this work, we study the challenge of imposing roles on open-domain dialogue systems, with the goal of making the systems maintain consistent roles while conversing naturally with humans.
Summary Level Training of Sentence Rewriting for Abstractive Summarization
As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then paraphrasing the selected ones to generate a summary.
Ranked #5 on
Extractive Text Summarization
on CNN / Daily Mail
Abstractive Text Summarization
Extractive Text Summarization
+2
SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem.
Ranked #6 on
Emotion Recognition in Conversation
on EC
SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem.
Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations
Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing.
