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Found 27 papers, 12 papers with code
Cross-Lingual Training of Dense Retrievers for Document Retrieval
Dense retrieval has shown great success for passage ranking in English.
Better Language Model with Hypernym Class Prediction
Class-based language models (LMs) have been long devised to address context sparsity in $n$-gram LMs.
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
Semi-global Periodic Event-triggered Output Regulation for Nonlinear Multi-agent Systems
This study focuses on periodic event-triggered (PET) cooperative output regulation problem for a class of nonlinear multi-agent systems.
Hierarchical Character Tagger for Short Text Spelling Error Correction
State-of-the-art approaches to spelling error correction problem include Transformer-based Seq2Seq models, which require large training sets and suffer from slow inference time; and sequence labeling models based on Transformer encoders like BERT, which involve token-level label space and therefore a large pre-defined vocabulary dictionary.
Prefix-to-SQL: Text-to-SQL Generation from Incomplete User Questions
Existing text-to-SQL research only considers complete questions as the input, but lay-users might strive to formulate a complete question.
Cross-Lingual Training with Dense Retrieval for Document Retrieval
Dense retrieval has shown great success in passage ranking in English.
Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval
We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations.
Logic-Consistency Text Generation from Semantic Parses
Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries.
End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task
Furthermore, we propose two value filling methods to build the bridge from the existing zero-shot semantic parsers to real-world applications, considering most of the existing parsers ignore the values filling in the synthesized SQL.
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).
Ranked #3 on
Semantic Parsing
on spider
Cross-Lingual Training of Neural Models for Document Ranking
We tackle the challenge of cross-lingual training of neural document ranking models for mono-lingual retrieval, specifically leveraging relevance judgments in English to improve search in non-English languages.
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL
Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any input.
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance.
Derivation of Elastic Wave Equation from New Motion Description
In classical mechanics, the motion of an object is described with Newton's three laws of motion, which means that the motion of the material elements composing a continuum can be described with the particle model.
Classical Physics Materials Science Fluid Dynamics
Segatron: Segment-Aware Transformer for Language Modeling and Understanding
To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token.
Ranked #9 on
Language Modelling
on WikiText-103
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2
Experimental results show that the Chinese GPT2 can generate better essay endings with \eop.
Cross-Lingual Relevance Transfer for Document Retrieval
Recent work has shown the surprising ability of multi-lingual BERT to serve as a zero-shot cross-lingual transfer model for a number of language processing tasks.
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling
A core problem of information retrieval (IR) is relevance matching, which is to rank documents by relevance to a user{'}s query.
Aligning Cross-Lingual Entities with Multi-Aspect Information
Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages.
Simple BERT Models for Relation Extraction and Semantic Role Labeling
We present simple BERT-based models for relation extraction and semantic role labeling.
Ranked #22 on
Relation Extraction
on TACRED
Matching Entities Across Different Knowledge Graphs with Graph Embeddings
This paper explores the problem of matching entities across different knowledge graphs.
Simple Attention-Based Representation Learning for Ranking Short Social Media Posts
This paper explores the problem of ranking short social media posts with respect to user queries using neural networks.
Farewell Freebase: Migrating the SimpleQuestions Dataset to DBpedia
To address this problem, we present SimpleDBpediaQA, a new benchmark dataset for simple question answering over knowledge graphs that was created by mapping SimpleQuestions entities and predicates from Freebase to DBpedia.
IcoRating: A Deep-Learning System for Scam ICO Identification
For the best setting, the proposed system is able to identify scam ICO projects with 0. 83 precision.
Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks
We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact.
