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HomeArtificial IntelligenceThe Berkeley Crossword Solver – The Berkeley Synthetic Intelligence Analysis Weblog

The Berkeley Crossword Solver – The Berkeley Synthetic Intelligence Analysis Weblog

We just lately printed the Berkeley Crossword Solver (BCS), the present state-of-the-art for fixing American-style crossword puzzles. The BCS combines neural query answering and probabilistic inference to attain near-perfect efficiency on most American-style crossword puzzles, just like the one proven under:

Determine 1: Instance American-style crossword puzzle

An earlier model of the BCS, along with Dr.Fill, was the primary laptop program to outscore all human rivals on the earth’s high crossword match. The newest model is the present top-performing system on crossword puzzles from The New York Occasions, reaching 99.7% letter accuracy (see the technical paper, internet demo, and code launch).

Crosswords are difficult for people and computer systems alike. Many clues are imprecise or underspecified and may’t be answered till crossing constraints are taken under consideration. Whereas some clues are much like factoid query answering, others require relational reasoning or understanding troublesome wordplay.

Listed here are a handful of instance clues from our dataset (solutions on the backside of this submit):

  • They’re given out at Berkeley’s HAAS Faculty (4)
  • Winter hrs. in Berkeley (3)
  • Area ender that UC Berkeley was one of many first colleges to undertake (3)
  • Angeleno at Berkeley, say (8)

The BCS makes use of a two-step course of to resolve crossword puzzles. First, it generates a chance distribution over potential solutions to every clue utilizing a query answering (QA) mannequin; second, it makes use of probabilistic inference, mixed with native search and a generative language mannequin, to deal with conflicts between proposed intersecting solutions.

Determine 2: Structure diagram of the Berkeley Crossword Solver

The BCS’s query answering mannequin relies on DPR (Karpukhin et al., 2020), which is a bi-encoder mannequin sometimes used to retrieve passages which can be related to a given query. Quite than passages, nevertheless, our method maps each questions and solutions right into a shared embedding house and finds solutions straight. In comparison with the earlier state-of-the-art technique for answering crossword clues, this method obtained a 13.4% absolute enchancment in top-1000 QA accuracy. We performed a handbook error evaluation and located that our QA mannequin sometimes carried out nicely on questions involving information, commonsense reasoning, and definitions, however it typically struggled to grasp wordplay or theme-related clues.

After working the QA mannequin on every clue, the BCS runs crazy perception propagation to iteratively replace the reply chances within the grid. This enables info from excessive confidence predictions to propagate to more difficult clues. After perception propagation converges, the BCS obtains an preliminary puzzle answer by greedily taking the best probability reply at every place.

The BCS then refines this answer utilizing a neighborhood search that tries to exchange low confidence characters within the grid. Native search works through the use of a guided proposal distribution by which characters that had decrease marginal chances throughout perception propagation are iteratively changed till a domestically optimum answer is discovered. We rating these alternate characters utilizing a character-level language mannequin (ByT5, Xue et al., 2022), that handles novel solutions higher than our closed-book QA mannequin.

Determine 3: Instance modifications made by our native search process

We evaluated the BCS on puzzles from 5 main crossword publishers, together with The New York Occasions. Our system obtains 99.7% letter accuracy on common, which jumps to 99.9% when you ignore puzzles that contain uncommon themes. It solves 81.7% of puzzles with no single mistake, which is a 24.8% enchancment over the earlier state-of-the-art system.

Determine 4: Outcomes in comparison with earlier state-of-the-art Dr.Fill

The American Crossword Puzzle Match (ACPT) is the most important and longest-running crossword match and is organized by Will Shortz, the New York Occasions crossword editor. Two prior approaches to laptop crossword fixing gained mainstream consideration and competed within the ACPT: Proverb and Dr.Fill. Proverb is a 1998 system that ranked 213th out of 252 rivals within the match. Dr.Fill’s first competitors was in ACPT 2012, and it ranked 141st out of 650 rivals. We teamed up with Dr.Fill’s creator Matt Ginsberg and mixed an early model of our QA system with Dr.Fill’s search process to outscore all 1033 human rivals within the 2021 ACPT. Our joint submission solved all seven puzzles in underneath a minute, lacking simply three letters throughout two puzzles.

Determine 5: Outcomes from the 2021 American Crossword Puzzle Match (ACPT)

We’re actually excited concerning the challenges that stay in crosswords, together with dealing with troublesome themes and extra complicated wordplay. To encourage future work, we’re releasing a dataset of 6.4M query reply clues, a demo of the Berkeley Crossword Solver, and our code at

Solutions to clues: MBAS, PST, EDU, INSTATER



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