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Found 14 papers, 4 papers with code
Enhanced Entity Annotations for Multilingual Corpora
WEXEA is a tool to exhaustively annotate entities in the English Wikipedia.
Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key Point Generation and Introducing an Automatic Coverage Evaluation Metric
This paper introduces a novel extractive approach for key point generation, that outperforms previous state-of-the-art methods for the task.
Recent Trends in Unsupervised Summarization
This survey covers different recent techniques and models used for unsupervised summarization.
Named Entity Recognition for Partially Annotated Datasets
The most common Named Entity Recognizers are usually sequence taggers trained on fully annotated corpora, i. e. the class of all words for all entities is known.
FREDA: Flexible Relation Extraction Data Annotation
To effectively train accurate Relation Extraction models, sufficient and properly labeled data is required.
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model
Multi-label emotion classification is an important task in NLP and is essential to many applications.
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)
In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning.
WEXEA: Wikipedia EXhaustive Entity Annotation
Building predictive models for information extraction from text, such as named entity recognition or the extraction of semantic relationships between named entities in text, requires a large corpus of annotated text.
Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation
Most of the existing methods treat this task as a problem of single-label multi-class text classification.
Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems
Self-attentional models are a new paradigm for sequence modelling tasks which differ from common sequence modelling methods, such as recurrence-based and convolution-based sequence learning, in the way that their architecture is only based on the attention mechanism.
Contrastive Reasons Detection and Clustering from Online Polarized Debate
This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints on polarized issues.
ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT
This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext.
Ranked #3 on
Emotion Recognition in Conversation
on EC
Emotion Recognition in Conversation
General Classification
+2
Automatic Dialogue Generation with Expressed Emotions
Despite myriad efforts in the literature designing neural dialogue generation systems in recent years, very few consider putting restrictions on the response itself.
