Unsupervised Commonsense Question Answering with Self-Talk

@article{Shwartz2020UnsupervisedCQ,
  title={Unsupervised Commonsense Question Answering with Self-Talk},
  author={Vered Shwartz and Peter West and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.05483}
}
Natural language understanding involves reading between the lines with implicit background knowledge. Current systems either rely on pre-trained language models as the sole implicit source of world knowledge, or resort to external knowledge bases (KBs) to incorporate additional relevant knowledge. We propose an unsupervised framework based on \emph{self-talk} as a novel alternative to multiple-choice commonsense tasks. Inspired by inquiry-based discovery learning (Bruner, 1961), our approach… 

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References

SHOWING 1-10 OF 76 REFERENCES

Urinary bladder hyporeflexia and reduced pain-related behaviour in P2X 3-deficient mice

TLDR
P2X3 is critical for peripheral pain responses and afferent pathways controlling urinary bladder volume reflexes and may have therapeutic potential in the treatment of disorders of urine storage and voiding such as overactive bladder.

The internal model principle of control theory

Conflict and context in peer relations.

Re-engineering Online Documentation: Designing Examples-based Online Support Systems

TLDR
It is argued that three developments in the design and evaluation of online support systems are reshaping traditional design efforts, and borrowing from principles of re-engineering, an alternative user-centered approach to online documentation design is outlined that draws on the metaphor of information as tool.

Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective

TLDR
This information-theoretic survey provides guidelines for the spectral efficiency gains possible through cognitive radios, as well as practical design ideas to mitigate the coexistence challenges in today's crowded spectrum.

Solving forward-backward stochastic differential equations explicitly — a four step scheme

SummaryIn this paper we investigate the nature of the adapted solutions to a class of forward-backward stochastic differential equations (SDEs for short) in which the forward equation is

XLNet: Generalized Autoregressive Pretraining for Language Understanding

TLDR
XLNet is proposed, a generalized autoregressive pretraining method that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and overcomes the limitations of BERT thanks to its autore progressive formulation.

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

TLDR
A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

Interpretation of Natural Language Rules in Conversational Machine Reading

TLDR
This paper formalise this task and develops a crowd-sourcing strategy to collect 37k task instances based on real-world rules and crowd-generated questions and scenarios to assess its difficulty by evaluating the performance of rule-based and machine-learning baselines.
...