SADAS: A Dialogue Assistant System Towards Remediating Norm Violations
in Bilingual Socio-Cultural Conversations
- URL: http://arxiv.org/abs/2402.01736v1
- Date: Mon, 29 Jan 2024 08:54:21 GMT
- Title: SADAS: A Dialogue Assistant System Towards Remediating Norm Violations
in Bilingual Socio-Cultural Conversations
- Authors: Yuncheng Hua, Zhuang Li, Linhao Luo, Kadek Ananta Satriadi, Tao Feng,
Haolan Zhan, Lizhen Qu, Suraj Sharma, Ingrid Zukerman, Zhaleh Semnani-Azad
and Gholamreza Haffari
- Abstract summary: Socially-Aware Dialogue Assistant System (SADAS) is designed to ensure that conversations unfold with respect and understanding.
Our system's novel architecture includes: (1) identifying the categories of norms present in the dialogue, (2) detecting potential norm violations, (3) evaluating the severity of these violations, and (4) implementing targeted remedies to rectify the breaches.
- Score: 56.31816995795216
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In today's globalized world, bridging the cultural divide is more critical
than ever for forging meaningful connections. The Socially-Aware Dialogue
Assistant System (SADAS) is our answer to this global challenge, and it's
designed to ensure that conversations between individuals from diverse cultural
backgrounds unfold with respect and understanding. Our system's novel
architecture includes: (1) identifying the categories of norms present in the
dialogue, (2) detecting potential norm violations, (3) evaluating the severity
of these violations, (4) implementing targeted remedies to rectify the
breaches, and (5) articulates the rationale behind these corrective actions. We
employ a series of State-Of-The-Art (SOTA) techniques to build different
modules, and conduct numerous experiments to select the most suitable backbone
model for each of the modules. We also design a human preference experiment to
validate the overall performance of the system. We will open-source our system
(including source code, tools and applications), hoping to advance future
research. A demo video of our system can be found
at:~\url{https://youtu.be/JqetWkfsejk}. We have released our code and software
at:~\url{https://github.com/AnonymousEACLDemo/SADAS}.
Related papers
- Evaluating and Modeling Attribution for Cross-Lingual Question Answering [80.4807682093432]
This work is the first to study attribution for cross-lingual question answering.
We collect data in 5 languages to assess the attribution level of a state-of-the-art cross-lingual QA system.
We find that a substantial portion of the answers is not attributable to any retrieved passages.
arXiv Detail & Related papers (2023-05-23T17:57:46Z) - Adversarial Transformer Language Models for Contextual Commonsense
Inference [14.12019824666882]
Contextualized or discourse aware commonsense inference is the task of generating coherent commonsense assertions.
Some problems with the task are: lack of controllability for topics of the inferred facts; lack of commonsense knowledge during training.
We develop techniques to address the aforementioned problems in the task.
arXiv Detail & Related papers (2023-02-10T18:21:13Z) - A Vector Quantized Approach for Text to Speech Synthesis on Real-World
Spontaneous Speech [94.64927912924087]
We train TTS systems using real-world speech from YouTube and podcasts.
Recent Text-to-Speech architecture is designed for multiple code generation and monotonic alignment.
We show thatRecent Text-to-Speech architecture outperforms existing TTS systems in several objective and subjective measures.
arXiv Detail & Related papers (2023-02-08T17:34:32Z) - CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog
Evaluation [75.60156479374416]
CGoDial is a new challenging and comprehensive Chinese benchmark for Goal-oriented Dialog evaluation.
It contains 96,763 dialog sessions and 574,949 dialog turns totally, covering three datasets with different knowledge sources.
To bridge the gap between academic benchmarks and spoken dialog scenarios, we either collect data from real conversations or add spoken features to existing datasets via crowd-sourcing.
arXiv Detail & Related papers (2022-11-21T16:21:41Z) - A New Generation of Perspective API: Efficient Multilingual
Character-level Transformers [66.9176610388952]
We present the fundamentals behind the next version of the Perspective API from Google Jigsaw.
At the heart of the approach is a single multilingual token-free Charformer model.
We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings.
arXiv Detail & Related papers (2022-02-22T20:55:31Z) - Advances and Challenges in Conversational Recommender Systems: A Survey [133.93908165922804]
We provide a systematic review of the techniques used in current conversational recommender systems (CRSs)
We summarize the key challenges of developing CRSs into five directions.
These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI)
arXiv Detail & Related papers (2021-01-23T08:53:15Z) - Robustness Testing of Language Understanding in Dialog Systems [33.30143655553583]
We conduct comprehensive evaluation and analysis with respect to the robustness of natural language understanding models.
We introduce three important aspects related to language understanding in real-world dialog systems, namely, language variety, speech characteristics, and noise perturbation.
We propose a model-agnostic toolkit LAUG to approximate natural perturbation for testing the robustness issues in dialog systems.
arXiv Detail & Related papers (2020-12-30T18:18:47Z) - NUANCED: Natural Utterance Annotation for Nuanced Conversation with
Estimated Distributions [36.00476428803116]
In this work, we attempt to build a user-centric dialogue system.
We first model the user preferences as estimated distributions over the system ontology and map the users' utterances to such distributions.
We build a new dataset named NUANCED that focuses on such realistic settings for conversational recommendation.
arXiv Detail & Related papers (2020-10-24T03:23:14Z) - A Unified System for Aggression Identification in English Code-Mixed and
Uni-Lingual Texts [25.15521897068512]
We introduce a unified and robust deep learning architecture which works for English code-mixed dataset and uni-lingual English dataset.
The devised system, uses psycho-linguistic features and very ba-sic linguistic features.
Our proposed system outperforms all the previous approaches on English code-mixed dataset and uni-lingual English dataset.
arXiv Detail & Related papers (2020-01-15T17:06:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.