Towards Explainable AI Writing Assistants for Non-native English
Speakers
- URL: http://arxiv.org/abs/2304.02625v1
- Date: Wed, 5 Apr 2023 17:51:36 GMT
- Title: Towards Explainable AI Writing Assistants for Non-native English
Speakers
- Authors: Yewon Kim, Mina Lee, Donghwi Kim, Sung-Ju Lee
- Abstract summary: We highlight the challenges faced by non-native speakers when using AI writing assistants to paraphrase text.
We observe that they face difficulties in assessing paraphrased texts generated by AI writing assistants, largely due to the lack of explanations accompanying the suggested paraphrases.
We propose four potential user interfaces to enhance the writing experience of NNESs using AI writing assistants.
- Score: 3.7953068443263174
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We highlight the challenges faced by non-native speakers when using AI
writing assistants to paraphrase text. Through an interview study with 15
non-native English speakers (NNESs) with varying levels of English proficiency,
we observe that they face difficulties in assessing paraphrased texts generated
by AI writing assistants, largely due to the lack of explanations accompanying
the suggested paraphrases. Furthermore, we examine their strategies to assess
AI-generated texts in the absence of such explanations. Drawing on the needs of
NNESs identified in our interview, we propose four potential user interfaces to
enhance the writing experience of NNESs using AI writing assistants. The
proposed designs focus on incorporating explanations to better support NNESs in
understanding and evaluating the AI-generated paraphrasing suggestions.
Related papers
- How Does the Disclosure of AI Assistance Affect the Perceptions of Writing? [29.068596156140913]
We study whether and how the disclosure of the level and type of AI assistance in the writing process would affect people's perceptions of the writing.
Our results suggest that disclosing the AI assistance in the writing process, especially if AI has provided assistance in generating new content, decreases the average quality ratings.
arXiv Detail & Related papers (2024-10-06T16:45:33Z) - WordDecipher: Enhancing Digital Workspace Communication with Explainable AI for Non-native English Speakers [11.242099987201573]
Non-native English speakers (NNES) face challenges in digital workspace communication.
Current AI-assisted writing tools are equipped with fluency enhancement and rewriting suggestions.
We propose WordDecipher, an explainable AI-assisted writing tool to enhance digital workspace communication.
arXiv Detail & Related papers (2024-04-10T13:40:29Z) - Audio-Visual Neural Syntax Acquisition [91.14892278795892]
We study phrase structure induction from visually-grounded speech.
We present the Audio-Visual Neural Syntax Learner (AV-NSL) that learns phrase structure by listening to audio and looking at images, without ever being exposed to text.
arXiv Detail & Related papers (2023-10-11T16:54:57Z) - A Neural-Symbolic Approach Towards Identifying Grammatically Correct
Sentences [0.0]
It is commonly accepted that it is crucial to have access to well-written text from valid sources to tackle challenges like text summarization, question-answering, machine translation, or even pronoun resolution.
We present a simplified way to validate English sentences through a novel neural-symbolic approach.
arXiv Detail & Related papers (2023-07-16T13:21:44Z) - Elaborative Simplification as Implicit Questions Under Discussion [51.17933943734872]
This paper proposes to view elaborative simplification through the lens of the Question Under Discussion (QUD) framework.
We show that explicitly modeling QUD provides essential understanding of elaborative simplification and how the elaborations connect with the rest of the discourse.
arXiv Detail & Related papers (2023-05-17T17:26:16Z) - VISAR: A Human-AI Argumentative Writing Assistant with Visual
Programming and Rapid Draft Prototyping [13.023911633052482]
VISAR is an AI-enabled writing assistant system designed to help writers brainstorm and revise hierarchical goals within their writing context.
It organizes argument structures through synchronized text editing and visual programming, and enhances persuasiveness with argumentation spark recommendations.
A controlled lab study confirmed the usability and effectiveness of VISAR in facilitating the argumentative writing planning process.
arXiv Detail & Related papers (2023-04-16T15:29:03Z) - Collaboration with Conversational AI Assistants for UX Evaluation:
Questions and How to Ask them (Voice vs. Text) [18.884080068561843]
We conducted a Wizard-of-Oz design probe study with 20 participants who interacted with simulated AI assistants via text or voice.
We found that participants asked for five categories of information: user actions, user mental model, help from the AI assistant, product and task information, and user demographics.
The text assistant was perceived as significantly more efficient, but both were rated equally in satisfaction and trust.
arXiv Detail & Related papers (2023-03-07T03:59:14Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - Learning to Selectively Learn for Weakly-supervised Paraphrase
Generation [81.65399115750054]
We propose a novel approach to generate high-quality paraphrases with weak supervision data.
Specifically, we tackle the weakly-supervised paraphrase generation problem by:.
obtaining abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion.
We demonstrate that our approach achieves significant improvements over existing unsupervised approaches, and is even comparable in performance with supervised state-of-the-arts.
arXiv Detail & Related papers (2021-09-25T23:31:13Z) - Structural Pre-training for Dialogue Comprehension [51.215629336320305]
We present SPIDER, Structural Pre-traIned DialoguE Reader, to capture dialogue exclusive features.
To simulate the dialogue-like features, we propose two training objectives in addition to the original LM objectives.
Experimental results on widely used dialogue benchmarks verify the effectiveness of the newly introduced self-supervised tasks.
arXiv Detail & Related papers (2021-05-23T15:16:54Z) - Building Low-Resource NER Models Using Non-Speaker Annotation [58.78968578460793]
Cross-lingual methods have had notable success in addressing these concerns.
We propose a complementary approach to building low-resource Named Entity Recognition (NER) models using non-speaker'' (NS) annotations.
We show that use of NS annotators produces results that are consistently on par or better than cross-lingual methods built on modern contextual representations.
arXiv Detail & Related papers (2020-06-17T03:24:38Z)
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.