Neural Program Induction for KBQA Without Gold Programs or Query Annotations

Neural Program Induction (NPI) is a paradigm for decomposing high-level tasks such as complex question-answering over knowledge bases (KBQA) into executable programs by employing neural models. Typically, this involves two key phases: i) inferring input program variables from the high-level task description, and ii) generating the correct program sequence involving these variables. Here we focus on NPI for Complex KBQA with only the final answer as supervision, and not gold programs. This raises major challenges; namely, i) noisy query annotation in the absence of any supervision can lead to catastrophic forgetting while learning, ii) reward becomes extremely sparse owing to the noise. To deal with these, we propose a noise-resilient NPI model, Stable Sparse Reward based Programmer (SSRP) that evades noise-induced instability through continual retrospection and its comparison with current learning behavior. On complex KBQA datasets, SSRP performs at par with hand-crafted rule-based models when provided with gold program input, and in the noisy settings outperforms state-of-the-art models by a significant margin even with a noisier query annotator.

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Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00262 ◽

2019 ◽

Vol 7 ◽

pp. 185-200 ◽

Cited By ~ 4

Author(s):

Amrita Saha ◽

Ghulam Ahmed Ansari ◽

Abhishek Laddha ◽

Karthik Sankaranarayanan ◽

Soumen Chakrabarti

Keyword(s):

Natural Language ◽

Question Answering ◽

Knowledge Bases ◽

Combinatorial Explosion ◽

Complex Queries ◽

Present Complex ◽

Complex Program ◽

Natural Language Query ◽

High Level ◽

Complex Question

Recent years have seen increasingly complex question-answering on knowledge bases (KBQA) involving logical, quantitative, and comparative reasoning over KB subgraphs. Neural Program Induction (NPI) is a pragmatic approach toward modularizing the reasoning process by translating a complex natural language query into a multi-step executable program. While NPI has been commonly trained with the ‘‘gold’’ program or its sketch, for realistic KBQA applications such gold programs are expensive to obtain. There, practically only natural language queries and the corresponding answers can be provided for training. The resulting combinatorial explosion in program space, along with extremely sparse rewards, makes NPI for KBQA ambitious and challenging. We present Complex Imperative Program Induction from Terminal Rewards (CIPITR), an advanced neural programmer that mitigates reward sparsity with auxiliary rewards, and restricts the program space to semantically correct programs using high-level constraints, KB schema, and inferred answer type. CIPITR solves complex KBQA considerably more accurately than key-value memory networks and neural symbolic machines (NSM). For moderately complex queries requiring 2- to 5-step programs, CIPITR scores at least 3× higher F1 than the competing systems. On one of the hardest class of programs (comparative reasoning) with 5–10 steps, CIPITR outperforms NSM by a factor of 89 and memory networks by 9 times. 1

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Meaningful Answer Generation of E-Commerce Question-Answering

ACM Transactions on Information Systems ◽

10.1145/3432689 ◽

2021 ◽

Vol 39 (2) ◽

pp. 1-26

Author(s):

Shen Gao ◽

Xiuying Chen ◽

Zhaochun Ren ◽

Dongyan Zhao ◽

Rui Yan

Keyword(s):

Large Scale ◽

Question Answering ◽

State Of The Art ◽

Neural Model ◽

Product Reviews ◽

Neural Models ◽

Product Attributes ◽

Human Evaluation ◽

Final Answer ◽

Answer Pattern

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of product-aware answer generation , which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems (i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called the Meaningful Product Answer Generator ( MPAG ), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct reasoning among these reviews . We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.

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Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/519 ◽

2020 ◽

Author(s):

Yongrui Chen ◽

Huiying Li ◽

Yuncheng Hua ◽

Guilin Qi

Keyword(s):

Knowledge Base ◽

Structure Prediction ◽

Question Answering ◽

State Transition ◽

Knowledge Bases ◽

Second Stage ◽

Transition Strategy ◽

Query Structure ◽

Two Stages ◽

Complex Question

Formal query building is an important part of complex question answering over knowledge bases. It aims to build correct executable queries for questions. Recent methods try to rank candidate queries generated by a state-transition strategy. However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries. In this paper, we propose a new formal query building approach that consists of two stages. In the first stage, we predict the query structure of the question and leverage the structure to constrain the generation of the candidate queries. We propose a novel graph generation framework to handle the structure prediction task and design an encoder-decoder model to predict the argument of the predetermined operation in each generative step. In the second stage, we follow the previous methods to rank the candidate queries. The experimental results show that our formal query building approach outperforms existing methods on complex questions while staying competitive on simple questions.

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Location-Aware Graph Convolutional Networks for Video Question Answering

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6737 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11021-11028 ◽

Cited By ~ 5

Author(s):

Deng Huang ◽

Peihao Chen ◽

Runhao Zeng ◽

Qing Du ◽

Mingkui Tan ◽

...

Keyword(s):

Question Answering ◽

State Of The Art ◽

Attention Mechanism ◽

Video Frame ◽

Location Information ◽

Object Interaction ◽

Location Aware ◽

Art Methods ◽

Final Answer ◽

Video Question Answering

We addressed the challenging task of video question answering, which requires machines to answer questions about videos in a natural language form. Previous state-of-the-art methods attempt to apply spatio-temporal attention mechanism on video frame features without explicitly modeling the location and relations among object interaction occurred in videos. However, the relations between object interaction and their location information are very critical for both action recognition and question reasoning. In this work, we propose to represent the contents in the video as a location-aware graph by incorporating the location information of an object into the graph construction. Here, each node is associated with an object represented by its appearance and location features. Based on the constructed graph, we propose to use graph convolution to infer both the category and temporal locations of an action. As the graph is built on objects, our method is able to focus on the foreground action contents for better video question answering. Lastly, we leverage an attention mechanism to combine the output of graph convolution and encoded question features for final answer reasoning. Extensive experiments demonstrate the effectiveness of the proposed methods. Specifically, our method significantly outperforms state-of-the-art methods on TGIF-QA, Youtube2Text-QA and MSVD-QA datasets.

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Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering

ACM Transactions on Computing for Healthcare ◽

10.1145/3465221 ◽

2021 ◽

Vol 2 (4) ◽

pp. 1-24

Author(s):

Pratyay Banerjee ◽

Kuntal Kumar Pal ◽

Murthy Devarakonda ◽

Chitta Baral

Keyword(s):

Question Answering ◽

State Of The Art ◽

Named Entity Recognition ◽

Entity Recognition ◽

Neural Models ◽

Named Entity ◽

Input Text ◽

The Impact ◽

Biomedical Named Entity Recognition ◽

Entity Class

In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e., B , I , O only) compared to multiple entity-classes in traditional NER (i.e., B entity 1, B entity 2, I entity 1, I , O ), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.

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NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning

Prague Bulletin of Mathematical Linguistics ◽

10.2478/pralin-2018-0010 ◽

2018 ◽

Vol 111 (1) ◽

pp. 113-124 ◽

Cited By ~ 3

Author(s):

Álvaro Peris ◽

Francisco Casacuberta

Keyword(s):

Machine Translation ◽

Question Answering ◽

State Of The Art ◽

Translation System ◽

Extended Version ◽

Neural Machine Translation ◽

Video Captioning ◽

High Level ◽

Translation Systems

Abstract We present NMT-Keras, a flexible toolkit for training deep learning models, which puts a particular emphasis on the development of advanced applications of neural machine translation systems, such as interactive-predictive translation protocols and long-term adaptation of the translation system via continuous learning. NMT-Keras is based on an extended version of the popular Keras library, and it runs on Theano and TensorFlow. State-of-the-art neural machine translation models are deployed and used following the high-level framework provided by Keras. Given its high modularity and flexibility, it also has been extended to tackle different problems, such as image and video captioning, sentence classification and visual question answering.

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