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Found 23 papers, 10 papers with code
Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks
Our analysis over the chain-of-thought generation of edited models further uncover key reasons behind the inadequacy of existing knowledge editing methods from a reasoning standpoint, involving aspects on fact-wise editing, fact recall ability, and coherence in generation.
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data.
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge.
Benchmarking Diverse-Modal Entity Linking with Generative Models
Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables.
UNITE: A Unified Benchmark for Text-to-SQL Evaluation
A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures.
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries.
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing
In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data.
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations
There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022).
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases
Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs.
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding
When synthesizing a SQL query, there is no explicit semantic information of NLQ available to the parser which leads to undesirable generalization performance.
REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction
Our generative model is a unified framework to sequentially generate relational triplets under various relation extraction settings and explicitly utilizes relevant knowledge from Knowledge Graph (KG) to resolve ambiguities.
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering
However, a large amount of world's knowledge is stored in structured databases, and need to be accessed using query languages such as SQL.
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.
Improving Factual Consistency of Abstractive Summarization via Question Answering
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents.
Generative Context Pair Selection for Multi-hop Question Answering
Compositional reasoning tasks like multi-hop question answering, require making latent decisions to get the final answer, given a question.
Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering
Experiments show that: (1) Our IR-based retrieval method is able to collect high-quality candidates efficiently, thus enables our method adapt to large-scale KBs easily; (2) the BERT model improves the accuracy across all three sub-tasks; and (3) benefiting from multi-task learning, the unified model obtains further improvements with only 1/3 of the original parameters.
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 #7 on
Text-To-SQL
on spider
(Exact Match Accuracy (Dev) metric)
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity.
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions.
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering
Training a QA model on this data gives a relative improvement over a previous unsupervised model in F1 score on the SQuAD dataset by about 14%, and 20% when the answer is a named entity, achieving state-of-the-art performance on SQuAD for unsupervised QA.
Universal Text Representation from BERT: An Empirical Study
We present a systematic investigation of layer-wise BERT activations for general-purpose text representations to understand what linguistic information they capture and how transferable they are across different tasks.
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering
To tackle this issue, we propose a multi-passage BERT model to globally normalize answer scores across all passages of the same question, and this change enables our QA model find better answers by utilizing more passages.
Ranked #3 on
Open-Domain Question Answering
on SearchQA
dna2vec: Consistent vector representations of variable-length k-mers
One of the ubiquitous representation of long DNA sequence is dividing it into shorter k-mer components.
