BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION
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Updated
Mar 25, 2019 - Python
BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION
Решение, занимающее 28/184 место в отборочном контесте ONTI "AI" на датасете MuSeRC.
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension
KorSQuAD-pl provides transfer learning codes about korean dataset KorQuAD and english dataset SQuAD for extractive question answering. KorSQuAD-pl implemented through pytorch lightning.
A PyTorch implementation of Neural Ranker-Reader model for Machine Reading Comprehension
Cooking Recipe MRC using PEFT techniques
Different from prior reseraches that only dive into Machine Reading Comprehension (MRC) approach, we compare the strong QA models in two scenarios: MRC (span extraction) and Answer Generation (AG) (Text Generation) for Vietnamese Legal Documents.
Machine Comprehension on Squad Dataset using Match-LSTM + Ans-Ptr Network
Fine-tuning Question Answering models on German with the GermanQuAD dataset
CS Bachelor Thesis. Open Domain Question Answering System that tries to answer general topic questions fetching from wikipedia.
This is a simple platform for labeling answers to questions in an article.
MRC question and answer approach using NLP and machine learning techniques
a new large-scale challenging dataset for CLRC (Cross-Lingual Reading Comprehension)
Source Code for "Teaching Machine Comprehension with Compositional Explanations" (Findings of EMNLP 2020)
The official implementation for ACL 2021 "Challenges in Information Seeking QA: Unanswerable Questions and Paragraph Retrieval".
Code for the paper "Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation" (COLING 2020)
Reasoning Shortcuts in MRC
Linguistic-knowledge-aware Neuro-symbolic Model for Entity State Tracking
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