scholarly journals Natural language understanding of map navigation queries in Roman Urdu by joint entity and intent determination

2021 ◽  
Vol 7 ◽  
pp. e615
Author(s):  
Javeria Hassan ◽  
Muhammad Ali Tahir ◽  
Adnan Ali
Keyword(s):  
Natural Language ◽  
Natural Language Understanding ◽  
Dialogue Systems ◽  
Word Embeddings ◽  
Dialogue System ◽  
Language Understanding ◽  
Input Sentence ◽  
Navigation Software ◽  
Task Oriented ◽  
Natural Way

Navigation based task-oriented dialogue systems provide users with a natural way of communicating with maps and navigation software. Natural language understanding (NLU) is the first step for a task-oriented dialogue system. It extracts the important entities (slot tagging) from the user’s utterance and determines the user’s objective (intent determination). Word embeddings are the distributed representations of the input sentence, and encompass the sentence’s semantic and syntactic representations. We created the word embeddings using different methods like FastText, ELMO, BERT and XLNET; and studied their effect on the natural language understanding output. Experiments are performed on the Roman Urdu navigation utterances dataset. The results show that for the intent determination task XLNET based word embeddings outperform other methods; while for the task of slot tagging FastText and XLNET based word embeddings have much better accuracy in comparison to other approaches.

scholarly journals Implementation of A Neural Natural Language Understanding Component for Arabic Dialogue Systems

2018 ◽  
Vol 142 ◽  
pp. 222-229 ◽  
Author(s):  
Abdallah M. Bashir ◽  
Abubakr Hassan ◽  
Benjamin Rosman ◽  
Daniel Duma ◽  
Mohanad Ahmed
Keyword(s):  
Natural Language ◽  
Natural Language Understanding ◽  
Dialogue Systems ◽  
Language Understanding

scholarly journals An Annotated Corpus of Reference Resolution for Interpreting Common Grounding

2020 ◽  
Vol 34 (05) ◽  
pp. 9081-9089
Author(s):  
Takuma Udagawa ◽  
Akiko Aizawa
Keyword(s):  
Natural Language ◽  
Natural Language Understanding ◽  
Fundamental Aspect ◽  
Dialogue Systems ◽  
Referring Expressions ◽  
Language Understanding ◽  
Reference Resolution ◽  
Quantitative Analyses ◽  
Partially Observable ◽  
Partial Understandings

Common grounding is the process of creating, repairing and updating mutual understandings, which is a fundamental aspect of natural language conversation. However, interpreting the process of common grounding is a challenging task, especially under continuous and partially-observable context where complex ambiguity, uncertainty, partial understandings and misunderstandings are introduced. Interpretation becomes even more challenging when we deal with dialogue systems which still have limited capability of natural language understanding and generation. To address this problem, we consider reference resolution as the central subtask of common grounding and propose a new resource to study its intermediate process. Based on a simple and general annotation schema, we collected a total of 40,172 referring expressions in 5,191 dialogues curated from an existing corpus, along with multiple judgements of referent interpretations. We show that our annotation is highly reliable, captures the complexity of common grounding through a natural degree of reasonable disagreements, and allows for more detailed and quantitative analyses of common grounding strategies. Finally, we demonstrate the advantages of our annotation for interpreting, analyzing and improving common grounding in baseline dialogue systems.

scholarly journals Common Ground, Frames and Slots for Comprehension in Dialogue Systems

2021 ◽  
Author(s):  
Philippe Blache ◽  
Matthis Houlès
Keyword(s):  
Natural Language ◽  
Common Ground ◽  
Bad News ◽  
Shared Knowledge ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Language Understanding ◽  
Language Generation ◽  
The Universe ◽  
Universe Of Discourse

This paper presents a dialogue system for training doctors to break bad news. The originality of this work lies in its knowledge representation. All information known before the dialogue (the universe of discourse, the context, the scenario of the dialogue) as well as the knowledge transferred from the doctor to the patient during the conversation is represented in a shared knowledge structure called common ground, that constitute the core of the system. The Natural Language Understanding and the Natural Language Generation modules of the system take advantage on this structure and we present in this paper different original techniques making it possible to implement them efficiently.

Sign in / Sign up

Export Citation Format

Share Document