Search Results for author:
Found 72 papers, 29 papers with code
Large-scale Word Alignment Using Soft Dependency Cohesion Constraints
In this paper, we take dependency cohesion as a soft constraint, and integrate it into a generative model for large-scale word alignment experiments.
Using a Dynamic Neural Field Model to Explore a Direct Collicular Inhibition Account of Inhibition of Return
When the interval between a transient ash of light (a "cue") and a second visual response signal (a "target") exceeds at least 200ms, responding is slowest in the direction indicated by the first signal.
FAQ-based Question Answering via Word Alignment
In this paper, we propose a novel word-alignment-based method to solve the FAQ-based question answering task.
Semi-supervised Clustering for Short Text via Deep Representation Learning
In this work, we propose a semi-supervised method for short text clustering, where we represent texts as distributed vectors with neural networks, and use a small amount of labeled data to specify our intention for clustering.
Sentence Similarity Learning by Lexical Decomposition and Composition
Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences.
Ranked #13 on
Question Answering
on WikiQA
Coverage Embedding Models for Neural Machine Translation
In this paper, we enhance the attention-based neural machine translation (NMT) by adding explicit coverage embedding models to alleviate issues of repeating and dropping translations in NMT.
Vocabulary Manipulation for Neural Machine Translation
Our method simply takes into account the translation options of each word or phrase in the source sentence, and picks a very small target vocabulary for each sentence based on a word-to-word translation model or a bilingual phrase library learned from a traditional machine translation model.
Sense Embedding Learning for Word Sense Induction
In the training stage, our method induces several sense centroids (embedding) for each polysemous word.
Ranked #4 on
Word Sense Induction
on SemEval 2010 WSI
Supervised Attentions for Neural Machine Translation
We simply compute the distance between the machine attentions and the "true" alignments, and minimize this cost in the training procedure.
AMR-to-text generation as a Traveling Salesman Problem
The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph.
Language Independent Dependency to Constituent Tree Conversion
We present a dependency to constituent tree conversion technique that aims to improve constituent parsing accuracies by leveraging dependency treebanks available in a wide variety in many languages.
Multi-Perspective Context Matching for Machine Comprehension
Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage.
Ranked #3 on
Open-Domain Question Answering
on SQuAD1.1
AMR-to-text Generation with Synchronous Node Replacement Grammar
This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar.
Bilateral Multi-Perspective Matching for Natural Language Sentences
Natural language sentence matching is a fundamental technology for a variety of tasks.
Ranked #17 on
Paraphrase Identification
on Quora Question Pairs
(Accuracy metric)
$k$-Nearest Neighbor Augmented Neural Networks for Text Classification
However, it lacks the capacity of utilizing instance-level information from individual instances in the training set.
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning.
Ranked #4 on
Open-Domain Question Answering
on Quasar
A Unified Query-based Generative Model for Question Generation and Question Answering
In the QG task, a question is generated from the system given the passage and the target answer, whereas in the QA task, the answer is generated given the question and the passage.
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer.
Ranked #1 on
Open-Domain Question Answering
on Quasar
Knowledge Base Relation Detection via Multi-View Matching
Relation detection is a core component for Knowledge Base Question Answering (KBQA).
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings.
Ranked #1 on
SQL-to-Text
on WikiSQL
A Graph-to-Sequence Model for AMR-to-Text Generation
The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph.
Ranked #1 on
Graph-to-Sequence
on LDC2015E86:
(using extra training data)
Leveraging Context Information for Natural Question Generation
The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer.
Ranked #11 on
Question Generation
on SQuAD1.1
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Existing neural semantic parsers mainly utilize a sequence encoder, i. e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees.
N-ary Relation Extraction using Graph State LSTM
Cross-sentence $n$-ary relation extraction detects relations among $n$ entities across multiple sentences.
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks
Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question.
Ranked #2 on
Question Answering
on COMPLEXQUESTIONS
SQL-to-Text Generation with Graph-to-Sequence Model
Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query.
N-ary Relation Extraction using Graph-State LSTM
Cross-sentence $n$-ary relation extraction detects relations among $n$ entities across multiple sentences.
Semantic Neural Machine Translation using AMR
It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models.
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs.
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering
However, extending KV-MemNNs to Knowledge Based Question Answering (KB-QA) is not trivia, which should properly decompose a complex question into a sequence of queries against the memory, and update the query representations to support multi-hop reasoning over the memory.
Multi-Granular Text Encoding for Self-Explaining Categorization
Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence.
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
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.
Domain Adaptation with BERT-based Domain Classification and Data Selection
The performance of deep neural models can deteriorate substantially when there is a domain shift between training and test data.
Leveraging Dependency Forest for Neural Medical Relation Extraction
Medical relation extraction discovers relations between entity mentions in text, such as research articles.
A Promotion Method for Generation Error Based Video Anomaly Detection
To address these problems, we propose a promotion method: utilize the maximum of block-level GEs on the frame to detect anomaly.
Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer
In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to.
Triplet Online Instance Matching Loss for Person Re-identification
Triplet loss processes batch construction in a complicated and fussy way and converges slowly.
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.
Master-Auxiliary: an efficient aggregation strategy for video anomaly detection
First, the aggregation strategy chooses one detector as master detector by experience, and sets the remaining detectors as auxiliary detectors.
Optimally Combining Classifiers for Semi-Supervised Learning
In this paper, we find these two methods have complementary properties and larger diversity, which motivates us to propose a new semi-supervised learning method that is able to adaptively combine the strengths of Xgboost and transductive support vector machine.
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.
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems
While many distributed optimization algorithms have been proposed for solving smooth or convex problems over the networks, few of them can handle non-convex and non-smooth problems.
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.
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)
Entity-level Factual Consistency of Abstractive Text Summarization
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document.
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.
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.
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.
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.
Larger Model Causes Lower Classification Accuracy Under Differential Privacy: Reason and Solution
Differential privacy (DP) is an essential technique for privacy-preserving, which works by adding random noise to the data.
Federated Semi-Supervised Learning with Class Distribution Mismatch
Similar to that encountered in federated supervised learning, class distribution of labeled/unlabeled data could be non-i. i. d.
Towards Understanding the Impact of Model Size on Differential Private Classification
Then we propose a feature selection method to reduce the size of the model, based on a new metric which trades off the classification accuracy and privacy preserving.
An Unbiased Symmetric Matrix Estimator for Topology Inference under Partial Observability
Network topology inference is a fundamental problem in many applications of network science, such as locating the source of fake news, brain connectivity networks detection, etc.
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.
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.
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.
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework
In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm.
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.
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.
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.
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.
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.
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations
However, existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications, impeding a comprehensive and unified evaluation of CLSP on a diverse range of NLs and MRs. To this end, we present XSemPLR, a unified benchmark for cross-lingual semantic parsing featured with 22 natural languages and 8 meaning representations by examining and selecting 9 existing datasets to cover 5 tasks and 164 domains.
NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data
Therefore, we open source a non-IID IoT person detection (NIPD) data set, which is collected from five different cameras.
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.
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel
This paper presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework to optimize the hyper-parameters.
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.
H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network
Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.
