Nan Yang

Computational model of geospatial semantic similarity based on ontology structure

2015 · Cehui kexue

Geospatial semantic similarity can be applied in spatial query,data retrieval and database generalization,and it is important for geospatial domain.Based on the analysis of the existing methods on semantic similarity,this paper proposed a computational model of …

Read + Verify: Machine Reading Comprehension with Unanswerable Questions

2018 · arXiv (Cornell University)

Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred. In addition to extract answers, previous works usually predict an additional "no-answer" probability to detect unanswerable cases. However, …

Unified Language Model Pre-training for Natural Language Understanding and Generation

2019 · arXiv (Cornell University)

This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, …

A Quantitative and Comparative Study of Syntactic Complexity of Subclasses of English Nominal Clauses

2025 · Journal of Quantitative Linguistics

This study examines the syntactic complexity of English nominal clauses, utilizing two key metrics – clause type and position – in a corpus-based analysis of 6,481 annotated sentences. The research targets subject, object, appositive, and …

Selective Encoding for Abstractive Sentence Summarization

2017

We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. The sentence encoder and decoder …

S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension

2017 · arXiv (Cornell University)

In this paper, we present a novel approach to machine reading comprehension for the MS-MARCO dataset. Unlike the SQuAD dataset that aims to answer a question with exact text spans in a passage, the MS-MARCO …

Gated Self-Matching Networks for Reading Comprehension and Question Answering

2017

In this paper, we present the gated selfmatching networks for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent …

Neural Document Summarization by Jointly Learning to Score and Select Sentences

2018

Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper, we present a novel end-to-end neural network framework for …

Attention-Guided Answer Distillation for Machine Reading Comprehension

2018

Despite that current reading comprehension systems have achieved significant advancements, their promising performances are often obtained at the cost of making an ensemble of numerous models. Besides, existing approaches are also vulnerable to adversarial attacks. …

Read + Verify: Machine Reading Comprehension with Unanswerable Questions

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred. In addition to extract answers, previous works usually predict an additional “no-answer” probability to detect unanswerable cases. However, …

Text Embeddings by Weakly-Supervised Contrastive Pre-training

2022 · arXiv (Cornell University)

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. The model is trained in a contrastive manner with weak supervision signals from our curated large-scale …