Search Results for author:
Found 31 papers, 9 papers with code
Dynamic Multistep Reasoning based on Video Scene Graph for Video Question Answering
Then, it performs multistep reasoning for better answer decision between the representations of the question and the video, and dynamically integrate the reasoning results.
BDKG at MEDIQA 2021: System Report for the Radiology Report Summarization Task
This paper presents our winning system at the Radiology Report Summarization track of the MEDIQA 2021 shared task.
Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering
It then performs dynamic reasoning based on the hierarchical representations of evidences to solve complex biomedical problems.
Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples
However, the existing method depends on the relevance between tasks and is prone to inter-type confusion. In this paper, we propose a novel two-stage framework Learn-and-Review (L&R) for continual NER under the type-incremental setting to alleviate the above issues. Specifically, for the learning stage, we distill the old knowledge from teacher to a student on the current dataset.
Continual Named Entity Recognition
named-entity-recognition
+2
A Transition-based Method for Complex Question Understanding
Existing works are based on end-to-end neural models which do not explicitly model the intermediate states and lack interpretability for the parsing process.
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction
Multi-triple extraction is a challenging task due to the existence of informative inter-triple correlations, and consequently rich interactions across the constituent entities and relations. While existing works only explore entity representations, we propose to explicitly introduce relation representation, jointly represent it with entities, and novelly align them to identify valid triples. We perform comprehensive experiments on document-level relation extraction and joint entity and relation extraction along with ablations to demonstrate the advantage of the proposed method.
Document-level Relation Extraction
Joint Entity and Relation Extraction
+2
$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference
In-Context Learning (ICL), which formulates target tasks as prompt completion conditioned on in-context demonstrations, has become the prevailing utilization of LLMs.
CLOP: Video-and-Language Pre-Training with Knowledge Regularizations
To this end, we propose a Cross-modaL knOwledge-enhanced Pre-training (CLOP) method with Knowledge Regularizations.
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance
Diffusion generative models have recently greatly improved the power of text-conditioned image generation.
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation
Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied.
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning
This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps.
Neural Knowledge Bank for Pretrained Transformers
The ability of pretrained Transformers to remember factual knowledge is essential but still limited for existing models.
Mixture of Experts for Biomedical Question Answering
In this paper, in order to alleviate the parameter competition problem, we propose a Mixture-of-Expert (MoE) based question answering method called MoEBQA that decouples the computation for different types of questions by sparse routing.
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning
Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on.
Building Chinese Biomedical Language Models via Multi-Level Text Discrimination
In this work we introduce eHealth, a Chinese biomedical PLM built from scratch with a new pre-training framework.
Link Prediction on N-ary Relational Facts: A Graph-based Approach
The key to our approach is to represent the n-ary structure of a fact as a small heterogeneous graph, and model this graph with edge-biased fully-connected attention.
Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN.
Ranked #3 on
Relation Extraction
on DocRED
Knowledge-Enhanced Named Entity Disambiguation for Short Text
Named entity disambiguation is an important task that plays the role of bridge between text and knowledge.
Multi-view Classification Model for Knowledge Graph Completion
This paper describes a novel multi-view classification model for knowledge graph completion, where multiple classification views are performed based on both content and context information for candidate triple evaluation.
Event Extraction as Multi-turn Question Answering
Most traditional approaches formulate this task as classification problems, with event types or argument roles taken as golden labels.
CoKE: Contextualized Knowledge Graph Embedding
This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings.
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network
Our method dynamically updates the representation of the knowledge according to the structural information of the constructed sub-graph.
Joint extraction of entities and overlapping relations using position-attentive sequence labeling
Joint entity and relation extraction is to detect entity and relation using a single model.
Ranked #2 on
Relation Extraction
on NYT-single
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension
In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC.
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling
Information selection is the most important component in document summarization task.
Ranked #32 on
Abstractive Text Summarization
on CNN / Daily Mail
Improving Neural Abstractive Document Summarization with Structural Regularization
Recent neural sequence-to-sequence models have shown significant progress on short text summarization.
Ranked #43 on
Abstractive Text Summarization
on CNN / Daily Mail
Answer-focused and Position-aware Neural Question Generation
(2) The model copies the context words that are far from and irrelevant to the answer, instead of the words that are close and relevant to the answer.
Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task
Current evaluation metrics to question answering based machine reading comprehension (MRC) systems generally focus on the lexical overlap between the candidate and reference answers, such as ROUGE and BLEU.
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension
While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with other answer candidates.
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine.
Ranked #3 on
Question Answering
on MS MARCO
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements.
