Weakly-Supervised Question Answering with Effective Rank and Weighted Loss over Candidates

2021 ◽  
Author(s):  
Haozhe Qin ◽  
Jiangang Zhu ◽  
Beijun Shen
Keyword(s):  
Question Answering ◽  
Weakly Supervised ◽  
Effective Rank

scholarly journals Weak Supervision Enhanced Generative Network for Question Generation

2019 ◽  
Author(s):  
Yutong Wang ◽  
Jiyuan Zheng ◽  
Qijiong Liu ◽  
Zhou Zhao ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Question Answering ◽  
Interaction Mechanism ◽  
Generation System ◽  
Dialogue System ◽  
Question Generation ◽  
Weak Supervision ◽  
Weakly Supervised ◽  
The Given ◽  
Automatic Question Generation

Automatic question generation according to an answer within the given passage is useful for many applications, such as question answering system, dialogue system, etc. Current neural-based methods mostly take two steps which extract several important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework to generate questions about these sentences. These approaches still acquire two steps and neglect the semantic relations between the answer and the context of the whole passage which is sometimes necessary for answering the question. To address this problem, we propose the Weakly Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions. More specifically, we devise a discriminator, Relation Guider, to capture the relations between the passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system. Experiments show the effectiveness of our method in both automatic evaluations and human evaluations.

scholarly journals Weakly-Supervised Open-Retrieval Conversational Question Answering

2021 ◽  
pp. 529-543
Author(s):  
Chen Qu ◽  
Liu Yang ◽  
Cen Chen ◽  
W. Bruce Croft ◽  
Kalpesh Krishna ◽  
...  
Keyword(s):  
Question Answering ◽  
Weakly Supervised

scholarly journals Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World

2013 ◽  
Vol 1 ◽  
pp. 193-206 ◽  
Author(s):  
Jayant Krishnamurthy ◽  
Thomas Kollar
Keyword(s):  
Language Acquisition ◽  
Natural Language ◽  
Physical Environment ◽  
Question Answering ◽  
Scene Understanding ◽  
Physical World ◽  
Training Procedure ◽  
Supervised Training ◽  
Weakly Supervised ◽  
Logical Semantics

This paper introduces Logical Semantics with Perception (LSP), a model for grounded language acquisition that learns to map natural language statements to their referents in a physical environment. For example, given an image, LSP can map the statement “blue mug on the table” to the set of image segments showing blue mugs on tables. LSP learns physical representations for both categorical (“blue,” “mug”) and relational (“on”) language, and also learns to compose these representations to produce the referents of entire statements. We further introduce a weakly supervised training procedure that estimates LSP’s parameters using annotated referents for entire statements, without annotated referents for individual words or the parse structure of the statement. We perform experiments on two applications: scene understanding and geographical question answering. We find that LSP outperforms existing, less expressive models that cannot represent relational language. We further find that weakly supervised training is competitive with fully supervised training while requiring significantly less annotation effort.

scholarly journals Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering

2021 ◽  
Author(s):  
Man Luo ◽  
Yankai Zeng ◽  
Pratyay Banerjee ◽  
Chitta Baral
Keyword(s):  
Question Answering ◽  
Knowledge Based ◽  
Weakly Supervised

scholarly journals Weakly Supervised Learning for Textbook Question Answering

2021 ◽  
Author(s):  
Jie Ma ◽  
Qi Chai ◽  
Jingyue Huang ◽  
Jun Liu ◽  
Yang You ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Text Comprehension ◽  
Question Answering ◽  
Weakly Supervised Learning ◽  
Effective Learning ◽  
Task Forces ◽  
Text Understanding ◽  
Current State ◽  
Weakly Supervised ◽  
Fine Tune

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multi-modal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, \emph{i.e.}, multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of $5.02\%$ and $4.12\%$ on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on \url{https://github.com/dr-majie/WSTQ}.

scholarly journals Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering

2020 ◽  
Author(s):  
Tao Shen ◽  
Xiubo Geng ◽  
Guodong Long ◽  
Jing Jiang ◽  
Chengqi Zhang ◽  
...  
Keyword(s):  
Question Answering ◽  
Logical Form ◽  
Computation Time ◽  
Search Space ◽  
Training Data ◽  
Semantic Parsing ◽  
Weak Supervision ◽  
Knowledge Based ◽  
Model Training ◽  
Weakly Supervised

Many algorithms for Knowledge-Based Question Answering (KBQA) depend on semantic parsing, which translates a question to its logical form. When only weak supervision is provided, it is usually necessary to search valid logical forms for model training. However, a complex question typically involves a huge search space, which creates two main problems: 1) the solutions limited by computation time and memory usually reduce the success rate of the search, and 2) spurious logical forms in the search results degrade the quality of training data. These two problems lead to a poorly-trained semantic parsing model. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. With search space constrained by predicted operators, sufficient search paths can be explored, more valid logical forms can be derived, and operators possibly causing spurious logical forms can be avoided. As a result, a larger proportion of questions in a weakly supervised training set are equipped with logical forms, and fewer spurious logical forms are generated. Such high-quality training data directly contributes to a better semantic parsing model. Experimental results on one of the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our approach and deliver a new state-of-the-art performance.

scholarly journals A Discrete Hard EM Approach for Weakly Supervised Question Answering

2019 ◽  
Author(s):  
Sewon Min ◽  
Danqi Chen ◽  
Hannaneh Hajishirzi ◽  
Luke Zettlemoyer
Keyword(s):  
Question Answering ◽  
Weakly Supervised

scholarly journals Weakly Supervised Learning for Textbook Question Answering

2021 ◽  
Author(s):  
Jie Ma ◽  
Qi Chai ◽  
Jingyue Huang ◽  
Jun Liu ◽  
Yang You ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Text Comprehension ◽  
Question Answering ◽  
Weakly Supervised Learning ◽  
Effective Learning ◽  
Task Forces ◽  
Text Understanding ◽  
Current State ◽  
Weakly Supervised ◽  
Fine Tune

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multi-modal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, \emph{i.e.}, multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of $5.02\%$ and $4.12\%$ on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on \url{https://github.com/dr-majie/WSTQ}.

Sign in / Sign up

Export Citation Format

Share Document