Search Results for author:
Found 8 papers, 4 papers with code
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models
Knowledge graph completion (KGC) aims to reason over known facts and infer the missing links.
Ranked #2 on
Link Prediction
on WN18RR
Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast
In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual) can be computed with a simple dot product.
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems
We propose a copy-augmented and feature-enriched sequence to sequence (seq2seq) model, which outperforms existing models by 3. 2% on the Math23K dataset and serves as a strong baseline of the Ape210K dataset.
Investigating Label Bias in Beam Search for Open-ended Text Generation
However, in open-ended text generation, beam search is often found to produce repetitive and generic texts, sampling-based decoding algorithms like top-k sampling and nucleus sampling are more preferred.
Denoising based Sequence-to-Sequence Pre-training for Text Generation
This paper presents a new sequence-to-sequence (seq2seq) pre-training method PoDA (Pre-training of Denoising Autoencoders), which learns representations suitable for text generation tasks.
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data
It is the first time copying words from the source context and fully pre-training a sequence to sequence model are experimented on the GEC task.
Ranked #4 on
Grammatical Error Correction
on JFLEG
Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension
Cloze-style reading comprehension has been a popular task for measuring the progress of natural language understanding in recent years.
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension
This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge.
