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Automated Crossword Solving

arXiv datasets Demo

This is the official code for the ACL 2022 paper Automated Crossword Solving. This repository contains the code for replicating training and running our Berkeley Crossword Solver system. We also release pretrained models, precomputed FAISS embeddings, and a crossword clue-answer dataset.

Those interested in the Matt Ginsberg's Dr.Fill solver can find it on the drfill branch. After the victory at the 2021 ACPT, the original Dr.Fill was retired. The Dr.Fill code is here primarily for archival purposes, and Matt Ginsberg is not in a position to provide assistance with it.



We use DPR as our retrieval system, and the TF-IDF implementation of DrQA during training. We use several models from HuggingFace Transformers, including GPT-2, BERT, and ByT5. Due to version incompatibilities with dependencies, we install both a Transformers 3.0 and 4.0 version.


  1. Clone the repo using git clone
  2. Fetch all submodules using git submodule update --init --recursive
  3. Make a new virtual environment with Python 3.8.
  4. Run This installs all dependencies and downloads checkpoints/embeddings.

Folder Structure

  • All training code is in train_scripts/
  • Data for training QA Model should be placed in data/.
  • All model checkpoints are located in checkpoints/.
  • All solver components are in solver/.

Running the Solver

The easiest way to get started is to run our pretrained checkpoints. Our solver is built to run either on a .json formatted puzzle or .puz files from the New York Times. Here is a sample .puz file from the NYT. Running the solver requires a GPU.

To run on a .json puzzle:

def solve(crossword):
    solver = BPSolver(crossword, max_candidates=500000)
    solution = solver.solve(num_iters=10, iterative_improvement_steps=5)
    print("*** Solver Output ***")
    print("*** Gold Solution ***")

with open(puzzle_file, "r") as f:
    print('Running solver on file:', f)
    puzzle = json.load(f)
crossword = Crossword(puzzle)

For .puz files, you should first convert them to .json files, then run the solving function above.

from utils import puz_to_json
puzzle = puz_to_json(puzzle_file)
crossword = Crossword(puzzle)

Decreasing max_candidates, num_iters, and iterative_improvement_steps can increase the solving speed, but will reduce accuracy.

Training From Scratch

Training the QA model

Our QA Model is a DPR biencoder model trained on a large crossword QA dataset.

Build the TF-IDF index:

bash train_scripts/biencoder/ path/to/dataset

This creates a TF-IDF index in the same folder as path/to/dataset.

Using this TF-IDF index, we can retrieve the top negative answers for each clue:

python3 train_scripts/biencoder/ \
    --model path/to/dataset/tfidf/ \
    --fills path/to/dataset/answers.jsonl \
    --clues path/to/dataset/docs.jsonl \
    --out path/to/dataset/ \

Train the biencoder using either BERT or RoBERTa encoders. We use BERT as we found it to perform better empirically.

CUDA_VISIBLE_DEVICES=0 bash train_scripts/biencoder/ \
    path/to/dataset/train.json \
    path/to/validation/validation.json \

Validation data should be formatted identically to the training .json file, but hard_negative_ctxs and negative_ctxs should both be zero-element lists.

Finally, we must precompute FAISS embeddings for all of our in-vocabulary answers so that retrieval of candidates is fast at solving time. Having multiple GPUs to shard this process can be helpful. Otherwise it can be quite slow. See the script to modify GPUs to shard across.

bash train_scripts/biencoder/ \
    checkpoints/biencoder/embeddings \
    checkpoints/biencoder/my_checkpoint \

Training the Segmenter Model

Coming soon!

Training the Local Search Models

Coming soon!

Crossword Dataset

The dataset is available through Huggingface datasets.

Rescraping Data

Coming soon!

Running the Demo

The demo is a simple frontend that requires cached states from the solver. To use it, first configure and run with your desired puzzles. It requires a single GPU. From the demo/ subfolder, you can then run python3 -m http.server to serve the demo.


Please consider citing our work if you found this code or our paper beneficial to your research.

  Author = {Eric Wallace and Nicholas Tomlin and Albert Xu and Kevin Yang and Eshaan Pathak and Matthew Ginsberg and Dan Klein},
  Booktitle = {Association for Computational Linguistics (ACL)},                            
  Year = {2022},
  Title = {Automated Crossword Solving}