scholarly journals A Pattern-Based Approach to Recognizing Time Expressions

2019 ◽  
Vol 33 ◽  
pp. 6335-6342
Author(s):  
Wentao Ding ◽  
Guanji Gao ◽  
Linfeng Shi ◽  
Yuzhong Qu
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Structural Information ◽  
Main Idea ◽  
Sequential Patterns ◽  
Semantic Parsing ◽  
Language Understanding ◽  
Fine Grained ◽  
Maximum Coverage ◽  
Approach Time

Recognizing time expressions is a fundamental and important task in many applications of natural language understanding, such as reading comprehension and question answering. Several newest state-of-the-art approaches have achieved good performance on recognizing time expressions. These approaches are black-boxed or based on heuristic rules, which leads to the difficulty in understanding the temporal information. On the contrary, classic rule-based or semantic parsing approaches can capture rich structural information, but their performances on recognition are not so good. In this paper, we propose a pattern-based approach, called PTime, which automatically generates and selects patterns for recognizing time expressions. In this approach, time expressions in training text are abstracted into type sequences by using fine-grained token types, thus the problem is transformed to select an appropriate subset of the sequential patterns. We use the Extended Budgeted Maximum Coverage (EBMC) model to optimize the pattern selection. The main idea is to maximize the correct token sequences matched by the selected patterns while the number of the mistakes should be limited by an adjustable budget. The interpretability of patterns and the adjustability of permitted number of mistakes make PTime a very promising approach for many applications. Experimental results show that PTime achieves a very competitive performance as compared with existing state-of-the-art approaches.

scholarly journals Multi-Point Semantic Representation for Intent Classification

2020 ◽  
Vol 34 (05) ◽  
pp. 9531-9538
Author(s):  
Jinghan Zhang ◽  
Yuxiao Ye ◽  
Yue Zhang ◽  
Likun Qiu ◽  
Bin Fu ◽  
...  
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Semantic Representation ◽  
Coarse Grained ◽  
Semantic Parsing ◽  
Language Understanding ◽  
Attention Model ◽  
Object Condition ◽  
Fine Grained ◽  
Query Type

Detecting user intents from utterances is the basis of natural language understanding (NLU) task. To understand the meaning of utterances, some work focuses on fully representing utterances via semantic parsing in which annotation cost is labor-intentsive. While some researchers simply view this as intent classification or frequently asked questions (FAQs) retrieval, they do not leverage the shared utterances among different intents. We propose a simple and novel multi-point semantic representation framework with relatively low annotation cost to leverage the fine-grained factor information, decomposing queries into four factors, i.e., topic, predicate, object/condition, query type. Besides, we propose a compositional intent bi-attention model under multi-task learning with three kinds of attention mechanisms among queries, labels and factors, which jointly combines coarse-grained intent and fine-grained factor information. Extensive experiments show that our framework and model significantly outperform several state-of-the-art approaches with an improvement of 1.35%-2.47% in terms of accuracy.

scholarly journals Relevance-guided Supervision for OpenQA with ColBERT

2021 ◽  
Vol 9 ◽  
pp. 929-944
Author(s):  
Omar Khattab ◽  
Christopher Potts ◽  
Matei Zaharia
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Training Data ◽  
Coarse Grained ◽  
Retrieval Model ◽  
Open Domain ◽  
Weak Supervision ◽  
Fine Grained ◽  
Vector Representations ◽  
Large Corpus

Abstract Systems for Open-Domain Question Answering (OpenQA) generally depend on a retriever for finding candidate passages in a large corpus and a reader for extracting answers from those passages. In much recent work, the retriever is a learned component that uses coarse-grained vector representations of questions and passages. We argue that this modeling choice is insufficiently expressive for dealing with the complexity of natural language questions. To address this, we define ColBERT-QA, which adapts the scalable neural retrieval model ColBERT to OpenQA. ColBERT creates fine-grained interactions between questions and passages. We propose an efficient weak supervision strategy that iteratively uses ColBERT to create its own training data. This greatly improves OpenQA retrieval on Natural Questions, SQuAD, and TriviaQA, and the resulting system attains state-of-the-art extractive OpenQA performance on all three datasets.

scholarly journals DeepStory: Video Story QA by Deep Embedded Memory Networks

2017 ◽  
Author(s):  
Kyung-Min Kim ◽  
Min-Oh Heo ◽  
Seong-Ho Choi ◽  
Byoung-Tak Zhang
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Video Stream ◽  
Long Term Memory ◽  
Embedded Memory ◽  
Term Memory ◽  
Attention Model ◽  
Fine Grained ◽  
Scene Description

Question-answering (QA) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings. Here we demonstrate the possibility of an AI agent performing video story QA by learning from a large amount of cartoon videos. We develop a video-story learning model, i.e. Deep Embedded Memory Networks (DEMN), to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data. The video stories are stored in a long-term memory component. For a given question, an LSTM-based attention model uses the long-term memory to recall the best question-story-answer triplet by focusing on specific words containing key information. We trained the DEMN on a novel QA dataset of children’s cartoon video series, Pororo. The dataset contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained sentences for scene description, and 8,913 story-related QA pairs. Our experimental results show that the DEMN outperforms other QA models. This is mainly due to 1) the reconstruction of video stories in a scene-dialogue combined form that utilize the latent embedding and 2) attention. DEMN also achieved state-of-the-art results on the MovieQA benchmark.

Facet Annotation by Extending CNN with a Matching Strategy

2018 ◽  
Vol 30 (6) ◽  
pp. 1647-1672 ◽  
Author(s):  
Bei Wu ◽  
Bifan Wei ◽  
Jun Liu ◽  
Zhaotong Guo ◽  
Yuanhao Zheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Question Answering ◽  
State Of The Art ◽  
Data Sets ◽  
Text Representation ◽  
Real World Data ◽  
Fine Grained ◽  
Community Question Answering ◽  
Matching Strategy ◽  
Similarity Matrices

Most community question answering (CQA) websites manage plenty of question-answer pairs (QAPs) through topic-based organizations, which may not satisfy users' fine-grained search demands. Facets of topics serve as a powerful tool to navigate, refine, and group the QAPs. In this work, we propose FACM, a model to annotate QAPs with facets by extending convolution neural networks (CNNs) with a matching strategy. First, phrase information is incorporated into text representation by CNNs with different kernel sizes. Then, through a matching strategy among QAPs and facet label texts (FaLTs) acquired from Wikipedia, we generate similarity matrices to deal with the facet heterogeneity. Finally, a three-channel CNN is trained for facet label assignment of QAPs. Experiments on three real-world data sets show that FACM outperforms the state-of-the-art methods.

scholarly journals Technical, Hard and Explainable Question Answering (THE-QA)

2019 ◽  
Author(s):  
Shailaja Sampat
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Knowledge Representation ◽  
Question Answering ◽  
State Of The Art ◽  
Future Research ◽  
Semantic Parsing ◽  
Learning Models ◽  
Knowledge Types ◽  
Language And Vision

The ability of an agent to rationally answer questions about a given task is the key measure of its intelligence. While we have obtained phenomenal performance over various language and vision tasks separately, 'Technical, Hard and Explainable Question Answering' (THE-QA) is a new challenging corpus which addresses them jointly. THE-QA is a question answering task involving diagram understanding and reading comprehension. We plan to establish benchmarks over this new corpus using deep learning models guided by knowledge representation methods. The proposed approach will envisage detailed semantic parsing of technical figures and text, which is robust against diverse formats. It will be aided by knowledge acquisition and reasoning module that categorizes different knowledge types, identify sources to acquire that knowledge and perform reasoning to answer the questions correctly. THE-QA data will present a strong challenge to the community for future research and will bridge the gap between state-of-the-art Artificial Intelligence (AI) and 'Human-level' AI.

scholarly journals How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context

2020 ◽  
Author(s):  
Qian Liu ◽  
Bei Chen ◽  
Jiaqi Guo ◽  
Jian-Guang Lou ◽  
Bin Zhou ◽  
...  
Keyword(s):  
Exploratory Study ◽  
State Of The Art ◽  
Context Modeling ◽  
Semantic Parsing ◽  
World Semantic ◽  
Research Directions ◽  
Fine Grained ◽  
Modeling Methods ◽  
Cross Domain ◽  
Shed Light

Recently semantic parsing in context has received a considerable attention, which is challenging since there are complex contextual phenomena. Previous works verified their proposed methods in limited scenarios, which motivates us to conduct an exploratory study on context modeling methods under real-world semantic parsing in context. We present a grammar-based decoding semantic parser and adapt typical context modeling methods on top of it. We evaluate 13 context modeling methods on two large complex cross-domain datasets, and our best model achieves state-of-the-art performances on both datasets with significant improvements. Furthermore, we summarize the most frequent contextual phenomena, with a fine-grained analysis on representative models, which may shed light on potential research directions. Our code is available at https://github.com/microsoft/ContextualSP.

scholarly journals Feature Augmented Memory with Global Attention Network for VideoQA

2020 ◽  
Author(s):  
Jiayin Cai ◽  
Chun Yuan ◽  
Cheng Shi ◽  
Lei Li ◽  
Yangyang Cheng ◽  
...  
Keyword(s):  
Question Answering ◽  
Temporal Order ◽  
State Of The Art ◽  
Memory Capacity ◽  
Coarse Grain ◽  
Fine Grained ◽  
Feature Representations ◽  
Benchmark Datasets ◽  
High Level ◽  
Video Question Answering

Recently, Recurrent Neural Network (RNN) based methods and Self-Attention (SA) based methods have achieved promising performance in Video Question Answering (VideoQA). Despite the success of these works, RNN-based methods tend to forget the global semantic contents due to the inherent drawbacks of the recurrent units themselves, while SA-based methods cannot precisely capture the dependencies of the local neighborhood, leading to insufficient modeling for temporal order. To tackle these problems, we propose a novel VideoQA framework which progressively refines the representations of videos and questions from fine to coarse grain in a sequence-sensitive manner. Specifically, our model improves the feature representations via the following two steps: (1) introducing two fine-grained feature-augmented memories to strengthen the information augmentation of video and text which can improve memory capacity by memorizing more relevant and targeted information. (2) appending the self-attention and co-attention module to the memory output thus the module is able to capture global interaction between high-level semantic informations. Experimental results show that our approach achieves state-of-the-art performance on VideoQA benchmark datasets.

scholarly journals Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering

2020 ◽  
Author(s):  
Zihao Zhu ◽  
Jing Yu ◽  
Yujing Wang ◽  
Yajing Sun ◽  
Yue Hu ◽  
...  
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Relevant Information ◽  
Convolutional Network ◽  
Graph Representations ◽  
Fine Grained ◽  
Visual Question Answering ◽  
Knowledge Reasoning ◽  
Final Answer ◽  
The Given

Fact-based Visual Question Answering (FVQA) requires external knowledge beyond the visible content to answer questions about an image. This ability is challenging but indispensable to achieve general VQA. One limitation of existing FVQA solutions is that they jointly embed all kinds of information without fine-grained selection, which introduces unexpected noises for reasoning the final answer. How to capture the question-oriented and information-complementary evidence remains a key challenge to solve the problem. In this paper, we depict an image by a multi-modal heterogeneous graph, which contains multiple layers of information corresponding to the visual, semantic and factual features. On top of the multi-layer graph representations, we propose a modality-aware heterogeneous graph convolutional network to capture evidence from different layers that is most relevant to the given question. Specifically, the intra-modal graph convolution selects evidence from each modality and cross-modal graph convolution aggregates relevant information across different graph layers. By stacking this process multiple times, our model performs iterative reasoning across three modalities and predicts the optimal answer by analyzing all question-oriented evidence. We achieve a new state-of-the-art performance on the FVQA task and demonstrate the effectiveness and interpretability of our model with extensive experiments.

scholarly journals Knowledge Graph Question Answering Using Graph-Pattern Isomorphism

2021 ◽  
Author(s):  
Daniel Vollmers ◽  
Rricha Jalota ◽  
Diego Moussallem ◽  
Hardik Topiwala ◽  
Axel-Cyrille Ngonga Ngomo ◽  
...  
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Art Performance ◽  
Training Examples ◽  
Ablation Study ◽  
Basic Graph

Knowledge Graph Question Answering (KGQA) systems are often based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.

scholarly journals Densely Connected Attention Flow for Visual Question Answering

2019 ◽  
Author(s):  
Fei Liu ◽  
Jing Liu ◽  
Zhiwei Fang ◽  
Richang Hong ◽  
Hanqing Lu
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Experimental Results ◽  
Effective Interactions ◽  
Fine Grained ◽  
Visual Question Answering ◽  
Complex Image ◽  
Hierarchical Levels ◽  
Answering Questions ◽  
Common Defect

Learning effective interactions between multi-modal features is at the heart of visual question answering (VQA). A common defect of the existing VQA approaches is that they only consider a very limited amount of interactions, which may be not enough to model latent complex image-question relations that are necessary for accurately answering questions. Therefore, in this paper, we propose a novel DCAF (Densely Connected Attention Flow) framework for modeling dense interactions. It densely connects all pairwise layers of the network via Attention Connectors, capturing fine-grained interplay between image and question across all hierarchical levels. The proposed Attention Connector efficiently connects the multi-modal features at any two layers with symmetric co-attention, and produces interaction-aware attention features. Experimental results on three publicly available datasets show that the proposed method achieves state-of-the-art performance.

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