Search Results for author:
Found 21 papers, 5 papers with code
AKEM: Aligning Knowledge Base to Queries with Ensemble Model for Entity Recognition and Linking
This paper presents a novel approach to address the Entity Recognition and Linking Challenge at NLPCC 2015.
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition
This paper concentrates on the understanding of interlocutors' emotions evoked in conversational utterances.
Ranked #17 on
Emotion Recognition in Conversation
on IEMOCAP
Event Extraction as Question Generation and Answering
In this paper, we propose QGA-EE, which enables a Question Generation (QG) model to generate questions that incorporate rich contextual information instead of using fixed templates.
A New Task and Dataset on Detecting Attacks on Human Rights Defenders
The ability to conduct retrospective analyses of attacks on human rights defenders over time and by location is important for humanitarian organizations to better understand historical or ongoing human rights violations and thus better manage the global impact of such events.
BUMP: A Benchmark of Unfaithful Minimal Pairs for Meta-Evaluation of Faithfulness Metrics
The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them.
Ol\'a, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer
We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian.
GTN-ED: Event Detection Using Graph Transformer Networks
Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection.
XFORMAL: A Benchmark for Multilingual Formality Style Transfer
We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian.
The ApposCorpus: A new multilingual, multi-domain dataset for factual appositive generation
News articles, image captions, product reviews and many other texts mention people and organizations whose name recognition could vary for different audiences.
A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management
In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems.
Cross-media Structured Common Space for Multimedia Event Extraction
We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents.
Cross-lingual Structure Transfer for Zero-resource Event Extraction
Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging.
Cross-lingual Structure Transfer for Relation and Event Extraction
The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages.
Visual Attention Model for Name Tagging in Multimodal Social Media
Everyday billions of multimodal posts containing both images and text are shared in social media sites such as Snapchat, Twitter or Instagram.
Platforms for Non-speakers Annotating Names in Any Language
We demonstrate two annotation platforms that allow an English speaker to annotate names for any language without knowing the language.
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System
We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap.
Entity-aware Image Caption Generation
Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images.
Embracing Non-Traditional Linguistic Resources for Low-resource Language Name Tagging
Current supervised name tagging approaches are inadequate for most low-resource languages due to the lack of annotated data and actionable linguistic knowledge.
