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Found 30 papers, 2 papers with code
On a Chatbot Navigating a User through a Concept-Based Knowledge Model
Information retrieval chatbots are widely used as assistants, to help users formulate their requirements about the products they want to purchase, and navigate to the set of items that satisfies their requirements in the best way.
Interrupt me Politely: Recommending Products and Services by Joining Human Conversation
We propose a novel way of conversational recommendation, where instead of asking questions to the user to acquire their preferences; the recommender tracks their conversation with other people, including customer support agents (CSA), and joins the conversation only when it is time to introduce a recommendation.
Correcting Texts Generated by Transformers using Discourse Features and Web Mining
We address both of these flaws: they are independent but can be combined to generate original texts that will be both consistent and truthful.
Relying on Discourse Analysis to Answer Complex Questions by Neural Machine Reading Comprehension
Machine reading comprehension (MRC) is one of the most challenging tasks in natural language processing domain.
DSNDM: Deep Siamese Neural Discourse Model with Attention for Text Pairs Categorization and Ranking
To this end, the neural TreeLSTM model is modified to effectively encode discourse trees and DSNDM model based on it is suggested to analyze pairs of texts.
Controlling Chat Bot Multi-Document Navigation with the Extended Discourse Trees
We define extended discourse trees, introduce means to manipulate with them, and outline scenarios of multi-document navigation to extend the abilities of the interactive information retrieval-based chat bot.
CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media
Thus, an interesting approach has emerged: to perform automatic fact-checking by verifying whether an input claim has been previously fact-checked by professional fact-checkers and to return back an article that explains their decision.
Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks
The use of contrastive loss for representation learning has become prominent in computer vision, and it is now getting attention in Natural Language Processing (NLP).
WhatTheWikiFact: Fact-Checking Claims Against Wikipedia
The rise of Internet has made it a major source of information.
Transformers: "The End of History" for NLP?
Recent advances in neural architectures, such as the Transformer, coupled with the emergence of large-scale pre-trained models such as BERT, have revolutionized the field of Natural Language Processing (NLP), pushing the state of the art for a number of NLP tasks.
aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning
We describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles.
Extract and Aggregate: A Novel Domain-Independent Approach to Factual Data Verification
It is widely accepted that factual data verification is a challenge even for the experts.
On a Chatbot Conducting Dialogue-in-Dialogue
We demo a chatbot that delivers content in the form of virtual dialogues automatically produced from plain texts extracted and selected from documents.
Discourse-Based Approach to Involvement of Background Knowledge for Question Answering
We introduce a concept of a virtual discourse tree to improve question answering (Q/A) recall for complex, multi-sentence questions.
On a Chatbot Providing Virtual Dialogues
We present a chatbot that delivers content in the form of virtual dialogues automatically produced from the plain texts that are extracted and selected from the documents.
Two Discourse Tree - Based Approaches to Indexing Answers
We explore anatomy of answers with respect to which text fragments from an answer are worth matching with a question and which should not be matched.
Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain
Among the contributions are the evaluation of various word embedding techniques on the different task types, with the findings that even embeddings trained on small corpora perform well for example on the word intrusion task.
Relation Extraction Datasets in the Digital Humanities Domain and their Evaluation with Word Embeddings
In this research, we manually create high-quality datasets in the digital humanities domain for the evaluation of language models, specifically word embedding models.
Building Dialogue Structure from Discourse Tree of a Question
In this section we propose a reasoning-based approach to a dialogue management for a customer support chat bot.
On a Chat Bot Finding Answers with Optimal Rhetoric Representation
The system achieves rhetoric agreement by learning pairs of discourse trees (DTs) for question (Q) and answer (A).
Chatbot with a Discourse Structure-Driven Dialogue Management
We then combine DTs for the paragraphs of documents to form what we call extended DT, which is a basis for interactive content exploration facilitated by the chat bot.
Extracting Social Networks from Literary Text with Word Embedding Tools
In this paper a social network is extracted from a literary text.
