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Zhihong Shao's Projects

aclpub icon aclpub

The official tool for creating proceedings for conferences of the Association for Computational Linguistics (ACL).

ambigqa icon ambigqa

An original implementation of EMNLP 2020, "AmbigQA: Answering Ambiguous Open-domain Questions"

atlas icon atlas

Code repository for supporting the paper "Atlas Few-shot Learning with Retrieval Augmented Language Models",(https//arxiv.org/abs/2208.03299)

cantor icon cantor

Code for "Chaining Simultaneous Thoughts for Numerical Reasoning"

cdial-gpt icon cdial-gpt

A Large-scale Chinese Short-Text Conversation Dataset and Chinese pre-training dialog models

cotk icon cotk

Conversational toolkits. A simple way to evaluate your model fairly!

dfgn-pytorch icon dfgn-pytorch

Code for ACL19 paper: Dynamically Fused Graph Network for Multihop Reasoning

docs icon docs

TensorFlow documentation

megatron-lm icon megatron-lm

Ongoing research training transformer models at scale

mimax icon mimax

Code and models for ``A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering (ACL 2021)``

qa-hard-em icon qa-hard-em

An original implementation of EMNLP 2019, "A Discrete Hard EM Approach for Weakly Supervised Question Answering"

rectify icon rectify

Code and models for ``Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework (ACL 2022)''

sae icon sae

Code for AAAI 2020 paper "Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents"

seal icon seal

Search Engines with Autoregressive Language models

spanbert icon spanbert

Code for using and evaluating SpanBERT.

torch-imle icon torch-imle

Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

transformers icon transformers

🤗 Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.

zhihongshao.github.io icon zhihongshao.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

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