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Methods to Structured Knowledge Grounding

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We present a collection of research papers that related to structured knowledge grounding tasks.

sk-encoding: Exploring structured knowledge encoding methods(concatenation of text and structured knowledge, positional embeddings design, manipulation in transformers etc.) on structured knowledge grounding tasks.

pre-training: Exploring pre-train(unsupervised training data source, self-supervised tasks etc.) on structured knowledge grounding tasks.

constrained-decoding: Exploring decoding methods(constrained decoding etc.) on structured knowledge grounding tasks.

unifying: Exploring unification of structured knowledge grounding tasks.

prompt-learning: Exploring prompt-learning methods on structured knowledge grounding tasks.


A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization. NIPS-19 sk-encoding

K-BERT: Enabling Language Representation with Knowledge Graph. AAAI-20 sk-encoding

TAPAS: Weakly Supervised Table Parsing via Pre-training. ACL-20 sk-encoding pre-training

A Simple Language Model for Task-Oriented Dialogue. NIPS-20 sk-encoding

TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data. ACL-20 sk-encoding pre-training

HittER: Hierarchical Transformers for Knowledge Graph Embeddings. EMNLP-21 sk-encoding

GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing. ICLR-21 pre-training

Multi-Task Pre-training for Plug-and-play Task-oriented Dialogue System. EMNLP-21 pre-training unifying

SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing. ICLR-21 sk-encoding pre-training

Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training. NAACL-21 sk-encoding pre-training

Structure-Grounded Pretraining for Text-to-SQL. NAACL-21 pre-training

Understanding tables with intermediate pre-training. EMNLP-20 pre-training

KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation. EMNLP-20 sk-encoding pre-training unifying

UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering. arxiv-20 sk-encoding unifying

JAKET: Joint Pre-training of Knowledge Graph and Language Understanding. arxiv-20 sk-encoding pre-training

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training. AAAI-21 pre-training

Table Fact Verification with Structure-Aware Transformer. ACL-20 sk-encoding

Structural Adapters in Pretrained Language Models for AMR-to-Text Generation. EMNLP-21 sk-encoding

Constrained Language Models Yield Few-Shot Semantic Parsers. EMNLP-21 sk-encoding constrained-decoding

Case-based Reasoning for Natural Language Queries over Knowledge Bases EMNLP-21 prompt-learning

DoT: An efficient Double Transformer for NLP tasks with tables. ACL-21 sk-encoding

Database Reasoning Over Text. ACL-21 sk-encoding

Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills. arxiv-21 pre-training

TAPEX: Table Pre-training via Learning a Neural SQL Executor. arxiv-21 pre-training

HTLM: Hyper-Text Pre-Training and Prompting of Language Models. arxiv-21 pre-training

MATE: Multi-view Attention for Table Transformer Efficiency. EMNLP-21 sk-encoding

RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering. arxiv-21 prompt-learning

Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System. arxiv-21 pre-training unifying

PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. EMNLP-21 constrained-decoding

FORTAP: Using Formulae for Numerical-Reasoning-Aware Table Pretraining. arxiv-21 pre-training

Learning To Retrieve Prompts for In-Context Learning arxiv-21 prompt-learning

Synchromesh: Reliable Code Generation from Pre-trained Language Models arxiv-22 constrained-decoding prompt-learning

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models. arxiv-22 unifying

TableFormer: Robust Transformer Modeling for Table-Text Encoding. ACL-22 sk-encoding

Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models. arxiv-22 prompt-learning

In-Context Learning for Few-Shot Dialogue State Tracking. arxiv-22 prompt-learning

T-RAG: End-to-End Table Question Answering via Retrieval-Augmented Generation. arxiv-22 sk-encoding