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Found 16 papers, 0 papers with code
Investigating the Effect of Conveying Understanding Results in Chat-Oriented Dialogue Systems
In this paper, we explored the effect of conveying understanding results of user utterances in a chat-oriented dialogue system by an experiment using human subjects.
Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization
Social media texts, such as tweets from Twitter, contain many types of non-standard tokens, and the number of normalization approaches for handling such noisy text has been increasing.
Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels
In this study, we investigated the effectiveness of augmented data for encoder-decoder-based neural normalization models.
Introduction method for argumentative dialogue using paired question-answering interchange about personality
The results suggest that inserting the question-answer dialogue enhances familiarity and naturalness.
Natural Language Inference with Definition Embedding Considering Context On the Fly
Natural language inference (NLI) is one of the most important tasks in NLP.
Role play-based question-answering by real users for building chatbots with consistent personalities
Having consistent personalities is important for chatbots if we want them to be believable.
Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension
Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages.
Commonsense Knowledge Base Completion and Generation
To improve the accuracy of CKB completion and expand the size of CKBs, we formulate a new commonsense knowledge base generation task (CKB generation) and propose a joint learning method that incorporates both CKB completion and CKB generation.
Multi-style Generative Reading Comprehension
Second, whereas previous studies built a specific model for each answer style because of the difficulty of acquiring one general model, our approach learns multi-style answers within a model to improve the NLG capability for all styles involved.
Ranked #1 on
Question Answering
on MS MARCO
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction
It enables QFE to consider the dependency among the evidence sentences and cover important information in the question sentence.
Ranked #61 on
Question Answering
on HotpotQA
A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension
Conversational machine comprehension (CMC) requires understanding the context of multi-turn dialogue.
Unsupervised Domain Adaptation of Language Models for Reading Comprehension
The second one is the proposed model that uses a multi-task learning approach of LM and RC.
Length-controllable Abstractive Summarization by Guiding with Summary Prototype
Unlike the previous models, our length-controllable abstractive summarization model incorporates a word-level extractive module in the encoder-decoder model instead of length embeddings.
Abstractive Summarization with Combination of Pre-trained Sequence-to-Sequence and Saliency Models
Experimental results showed that most of the combination models outperformed a simple fine-tuned seq-to-seq model on both the CNN/DM and XSum datasets even if the seq-to-seq model is pre-trained on large-scale corpora.
