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Found 49 papers, 14 papers with code
ANTUSD: A Large Chinese Sentiment Dictionary
This paper introduces the augmented NTU sentiment dictionary, abbreviated as ANTUSD, which is constructed by collecting sentiment stats of words in several sentiment annotation work.
UTCNN: a Deep Learning Model of Stance Classificationon on Social Media Text
Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0. 755 macro-average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld.
Whose Nickname is This? Recognizing Politicians from Their Aliases
In this work we also introduce two new datasets consisting of 167 phonemic pairs and 279 mixed pairs of aliases and formal names.
WordForce: Visualizing Controversial Words in Debates
This paper presents WordForce, a system powered by the state of the art neural network model to visualize the learned user-dependent word embeddings from each post according to the post content and its engaged users.
Chinese Textual Sentiment Analysis: Datasets, Resources and Tools
The basic processing tools are from CKIP Participants can download these resources, use them and solve the problems they encounter in this tutorial.
UTCNN: a Deep Learning Model of Stance Classification on Social Media Text
Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0. 755 macro average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld.
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.
Challenges in Providing Automatic Affective Feedback in Instant Messaging Applications
Instant messaging is one of the major channels of computer mediated communication.
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.
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.
Enabling Transitivity for Lexical Inference on Chinese Verbs Using Probabilistic Soft Logic
However, most of the lexical inference models with good performance are for nouns or noun phrases, which cannot be directly applied to the inference on events or states.
Natural Language Inference
Open-Domain Question Answering
+1
NLPSA at IJCNLP-2017 Task 2: Imagine Scenario: Leveraging Supportive Images for Dimensional Sentiment Analysis
Categorical sentiment classification has drawn much attention in the field of NLP, while less work has been conducted for dimensional sentiment analysis (DSA).
EmotionLines: An Emotion Corpus of Multi-Party Conversations
A total of 29, 245 utterances from 2, 000 dialogues are labeled in EmotionLines.
Using Inter-Sentence Diverse Beam Search to Reduce Redundancy in Visual Storytelling
Visual storytelling includes two important parts: coherence between the story and images as well as the story structure.
SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues
This paper describes an overview of the Dialogue Emotion Recognition Challenge, EmotionX, at the Sixth SocialNLP Workshop, which recognizes the emotion of each utterance in dialogues.
Dixit: Interactive Visual Storytelling via Term Manipulation
In this paper, we introduce Dixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence.
UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering
In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called "one hop".
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.
Entropy-Enhanced Multimodal Attention Model for Scene-Aware Dialogue Generation
After the entropy-enhanced DMN secures the video context, we apply an attention model that in-corporates summary and caption to generate an accurate answer given the question about the video.
SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and Chats
We present an overview of the EmotionX 2019 Challenge, held at the 7th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with IJCAI 2019.
Knowledge-Enriched Visual Storytelling
This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories.
Multi-step Joint-Modality Attention Network for Scene-Aware Dialogue System
Understanding dynamic scenes and dialogue contexts in order to converse with users has been challenging for multimodal dialogue systems.
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation
To generate inspired headlines, we propose a novel framework called POpularity-Reinforced Learning for inspired Headline Generation (PORL-HG).
MVIN: Learning Multiview Items for Recommendation
From the entity view, the mixing layer contrasts layer-wise GCN information to further obtain comprehensive features from internal entity-entity interactions in the KG.
Reactive Supervision: A New Method for Collecting Sarcasm Data
Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data.
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.
SocialNLP EmotionGIF 2020 Challenge Overview: Predicting Reaction GIF Categories on Social Media
We present an overview of the EmotionGIF2020 Challenge, held at the 8th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with ACL 2020.
Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing
Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair pay.
Plot and Rework: Modeling Storylines for Visual Storytelling
Writing a coherent and engaging story is not easy.
Ranked #27 on
Visual Storytelling
on VIST
Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter
We show how to augment the data with induced emotion and induced sentiment labels.
Stretch-VST: Getting Flexible With Visual Stories
Therefore, we propose to {``}stretch{''} the stories, which create the potential to present in-depth visual details.
Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction
There has been significant progress in utilizing weakly supervised approaches, which require only a small set of seed words for training aspect classifiers.
Let's Talk! Striking Up Conversations via Conversational Visual Question Generation
An engaging and provocative question can open up a great conversation.
Ask to Know More: Generating Counterfactual Explanations for Fake Claims
In this paper, we propose elucidating fact checking predictions using counterfactual explanations to help people understand why a specific piece of news was identified as fake.
Multi-VQG: Generating Engaging Questions for Multiple Images
These results open up an exciting challenge for visual-and-language models to implicitly construct a story behind a series of photos to allow for creativity and experience sharing and hence draw attention to downstream applications.
Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification
Fine-grained emotion classification (FEC) is a challenging task.
HonestBait: Forward References for Attractive but Faithful Headline Generation
Current methods for generating attractive headlines often learn directly from data, which bases attractiveness on the number of user clicks and views.
Location-Aware Visual Question Generation with Lightweight Models
This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location.
LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis
Thematic analysis (TA) has been widely used for analyzing qualitative data in many disciplines and fields.
Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term
In this study, we compare the effectiveness of a warning label and the state-of-the-art counterfactual explanations generated by GPT-4 in debunking misinformation.
MAAIG: Motion Analysis And Instruction Generation
Many people engage in self-directed sports training at home but lack the real-time guidance of professional coaches, making them susceptible to injuries or the development of incorrect habits.
Lying Through One’s Teeth: A Study on Verbal Leakage Cues
Although many studies use the LIWC lexicon to show the existence of verbal leakage cues in lie detection datasets, none mention how verbal leakage cues are influenced by means of data collection, or the impact thereof on the performance of models.
Learning to Rank Visual Stories From Human Ranking Data
In this paper, we present the VHED (VIST Human Evaluation Data) dataset, which first re-purposes human evaluation results for automatic evaluation; hence we develop Vrank (VIST Ranker), a novel reference-free VIST metric for story evaluation.
