Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP
@article{Rozner2021DecryptingCC, title={Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP}, author={Josh Rozner and Christopher Potts and Kyle Mahowald}, journal={ArXiv}, year={2021}, volume={abs/2104.08620} }
Cryptic crosswords, the dominant crossword variety in the UK, are a promising target for advancing NLP systems that seek to process semantically complex, highly compositional language. Cryptic clues read like fluent natural language but are adversarially composed of two parts: a definition and a wordplay cipher requiring character-level manipulations. Expert humans use creative intelligence to solve cryptics, flexibly combining linguistic, world, and domain knowledge. In this paper, we make two…
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References
SHOWING 1-10 OF 42 REFERENCES
Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language
- Computer ScienceEMNLP
- 2021
This work presents Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced, and on par with the accuracy of a rule-based clue solver.
“The Penny Drops”: Investigating Insight Through the Medium of Cryptic Crosswords
- Computer ScienceFront. Psychol.
- 2018
It is argued that the crossword paradigm overcomes many of the issues which beset other insight problems: for example, solution rates of cryptic crossword clues are high; new material can easily be commissioned, leading to a limitless pool of test items; and each puzzle contains clues resembling a wide variety of insight problem types, permitting a comparison of heterogeneous solving mechanisms within the same medium.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- Computer ScienceJ. Mach. Learn. Res.
- 2020
This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
- Computer ScienceEMNLP
- 2018
SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, finds that it is possible to achieve comparable accuracy to direct subword training from raw sentences.
Pun Generation with Surprise
- Computer ScienceNAACL
- 2019
An unsupervised approach to pun generation based on lots of raw (unhumorous) text and a surprisal principle is proposed, which posit that in a pun sentence, there is a strong association between the pun word and the distant context, but a strong associations between the alternativeword and the immediate context.
Using the BNC to produce dialectic cryptic crossword clues
- Computer Science
- 2001
This paper describes an attempt to generate seemingly meaningful cryptic crossword clues without trying to analyse meaning but relying solely on word occurrence statistics. It is a continuation of a…
Language Models are Unsupervised Multitask Learners
- Computer Science
- 2019
It is demonstrated that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText, suggesting a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Computer ScienceNAACL
- 2019
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.