Search Results for author:
Found 20 papers, 7 papers with code
Guided K-best Selection for Semantic Parsing Annotation
Collecting data for conversational semantic parsing is a time-consuming and demanding process.
Distilling Salient Reviews with Zero Labels
To alleviate this scarcity problem, we develop an unsupervised method, ZL-Distiller, which leverages contextual language representations of the reviews and their distributional patterns to identify salient sentences about entities.
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.
Extracting COVID-19 Events from Twitter
Language Modelling
+4
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings
Crafting effective captions for figures is important.
GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions
We first constructed SCICAP-EVAL, a human evaluation dataset that contains human judgments for 3, 600 scientific figure captions, both original and machine-made, for 600 arXiv figures.
Good Data, Large Data, or No Data? Comparing Three Approaches in Developing Research Aspect Classifiers for Biomedical Papers
In this study, we investigate the impact of different datasets on model performance for the crowd-annotated CODA-19 research aspect classification task.
ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing
The paper further discusses the practical human usage patterns in interacting with ConvXAI for scientific co-writing.
What Types of Questions Require Conversation to Answer? A Case Study of AskReddit Questions
The proliferation of automated conversational systems such as chatbots, spoken-dialogue systems, and smart speakers, has significantly impacted modern digital life.
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Prior work often treated figure caption generation as a vision-to-language task.
Conveying the Predicted Future to Users: A Case Study of Story Plot Prediction
Next, we conducted a preliminary user study using a story continuation task where AMT workers were given access to machine-generated story plots and asked to write a follow-up story.
Semantic Frame Forecast
In this paper, we formulate a long story as a sequence of "story blocks," where each block contains a fixed number of sentences (e. g., 10, 100, or 200).
Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent
The results of the user study show that the proposed agent can find out example sentences that help students learn more easily and efficiently.
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.
Extracting COVID-19 Events from Twitter
Language Modelling
+5
CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the COVID-19 Open Research Dataset
This paper introduces CODA-19, a human-annotated dataset that codes the Background, Purpose, Method, Finding/Contribution, and Other sections of 10, 966 English abstracts in the COVID-19 Open Research Dataset.
From Receptive to Productive: Learning to Use Confusing Words through Automatically Selected Example Sentences
In this study, students learn to differentiate the confusing words by reading the example sentences, and then choose the appropriate word(s) to complete the sentence translation task.
Visual Story Post-Editing
We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task.
MoodSwipe: A Soft Keyboard that Suggests MessageBased on User-Specified Emotions
We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data.
MoodSwipe: A Soft Keyboard that Suggests Messages Based on User-Specified Emotions
We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data.
Challenges in Providing Automatic Affective Feedback in Instant Messaging Applications
Instant messaging is one of the major channels of computer mediated communication.
Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners
In this paper, we propose GiveMeExample that ranks example sentences according to their capacity of demonstrating the differences among English and Chinese near-synonyms for language learners.
