Corpus ID: 233297028

Question Decomposition with Dependency Graphs

@article{Hasson2021QuestionDW,
  title={Question Decomposition with Dependency Graphs},
  author={Matan Hasson and Jonathan Berant},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.08647}
}
QDMR is a meaning representation for complex questions, which decomposes questions into a sequence of atomic steps. While stateof-the-art QDMR parsers use the common sequence-to-sequence (seq2seq) approach, a QDMR structure fundamentally describes labeled relations between spans in the input question, and thus dependency-based approaches seem appropriate for this task. In this work, we present a QDMR parser that is based on dependency graphs (DGs), where nodes in the graph are words and edges… Expand
1 Citations
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References

SHOWING 1-10 OF 30 REFERENCES
Break It Down: A Question Understanding Benchmark
TLDR
This work introduces a Question Decomposition Meaning Representation (QDMR) for questions, and demonstrates the utility of QDMR by showing that it can be used to improve open-domain question answering on the HotpotQA dataset, and can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Expand
Simpler but More Accurate Semantic Dependency Parsing
TLDR
The LSTM-based syntactic parser of Dozat and Manning (2017) is extended to train on and generate graph structures that aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. Expand
Compositional Semantic Parsing on Semi-Structured Tables
TLDR
This paper proposes a logical-form driven parsing algorithm guided by strong typing constraints and shows that it obtains significant improvements over natural baselines and is made publicly available. Expand
The Web as a Knowledge-Base for Answering Complex Questions
TLDR
This paper proposes to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers, and empirically demonstrates that question decomposition improves performance from 20.8 precision@1 to 27.5 precision @1 on this new dataset. Expand
Multi-hop Reading Comprehension through Question Decomposition and Rescoring
TLDR
A system that decomposes a compositional question into simpler sub-questions that can be answered by off-the-shelf single-hop RC models is proposed and a new global rescoring approach is introduced that considers each decomposition to select the best final answer, greatly improving overall performance. Expand
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong andExpand
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
TLDR
It is shown that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions. Expand
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
TLDR
HybridQA is presented, a new large-scale question-answering dataset that requires reasoning on heterogeneous information and can serve as a challenging benchmark to study question answering withheterogeneous information. Expand
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
TLDR
This paper incorporates copying into neural network-based Seq2Seq learning and proposes a new model called CopyNet with encoder-decoder structure which can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence. Expand
Constructing Datasets for Multi-hop Reading Comprehension Across Documents
TLDR
A novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods, in which a model learns to seek and combine evidence — effectively performing multihop, alias multi-step, inference. Expand
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