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Found 24 papers, 12 papers with code
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Neural abstractive summarization models have led to promising results in summarizing relatively short documents.
Ranked #4 on
Unsupervised Extractive Summarization
on Pubmed
Abstractive Text Summarization
Unsupervised Extractive Summarization
Scoring Sentence Singletons and Pairs for Abstractive Summarization
There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.
KPQA: A Metric for Generative Question Answering Using Keyphrase Weights
To evaluate our metric, we create high-quality human judgments of correctness on two GenQA datasets.
Understanding Points of Correspondence between Sentences for Abstractive Summarization
We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences.
ViLBERTScore: Evaluating Image Caption Using Vision-and-Language BERT
In this paper, we propose an evaluation metric for image captioning systems using both image and text information.
Learning to Fuse Sentences with Transformers for Summarization
The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts.
X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering
We extensively evaluate our framework on two challenging cross-lingual NLU tasks: multilingual task-oriented dialog and typologically diverse question answering.
TutorialVQA: Question Answering Dataset for Tutorial Videos
The results indicate that the task is challenging and call for the investigation of new algorithms.
CAISE: Conversational Agent for Image Search and Editing
To our knowledge, this is the first dataset that provides conversational image search and editing annotations, where the agent holds a grounded conversation with users and helps them to search and edit images according to their requests.
Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks
In this study, we propose a novel graph neural network called propagate-selector (PS), which propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation.
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion
We present an empirical study in favor of a cascade architecture to neural text summarization.
Detecting Table Region in PDF Documents Using Distant Supervision
Extensive evaluations demonstrate that our paradigm is compatible enough to leverage various features and learning models for open-domain table region detection within PDF files.
A Compare-Aggregate Model with Latent Clustering for Answer Selection
In this paper, we propose a novel method for a sentence-level answer-selection task that is a fundamental problem in natural language processing.
Ranked #8 on
Question Answering
on TrecQA
Analyzing Sentence Fusion in Abstractive Summarization
While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences.
Adjusting Image Attributes of Localized Regions with Low-level Dialogue
Our system grounds language on the level of edit operations, and suggests options for a user to choose from.
A Multimodal Dialogue System for Conversational Image Editing
In this paper, we present a multimodal dialogue system for Conversational Image Editing.
DSTC8-AVSD: Multimodal Semantic Transformer Network with Retrieval Style Word Generator
Audio Visual Scene-aware Dialog (AVSD) is the task of generating a response for a question with a given scene, video, audio, and the history of previous turns in the dialog.
Efficient Deployment of Conversational Natural Language Interfaces over Databases
Many users communicate with chatbots and AI assistants in order to help them with various tasks.
A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents
Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction.
AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises
With the renaissance of deep learning, neural networks have achieved promising results on many natural language understanding (NLU) tasks.
End-to-end Neural Coreference Resolution Revisited: A Simple yet Effective Baseline
Since the first end-to-end neural coreference resolution model was introduced, many extensions to the model have been proposed, ranging from using higher-order inference to directly optimizing evaluation metrics using reinforcement learning.
