A major obstacle to the wide-spread adoption of neural retrieval models is that they require
large supervised training sets to surpass traditional term-based techniques, which are …

Word embeddings and convolutional neural networks (CNN) have attracted extensive
attention in various classification tasks for Twitter, eg sentiment classification. However, the …

We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically
solving crossword puzzles. Our system works by generating answer candidates for each …

The progress of the natural language understanding (NLU) community has put forward
higher demands for the knowledge reserve and reasoning ability of the model. However …

Although the development of AI in games is remarkable, intelligent machines still lag behind
humans in games that require the ability of language understanding. In this paper, we focus …

Crossword puzzles are popular word games played in various languages around the world,
with diverse styles across different countries. For this reason, automated crossword solvers …

Current NLP datasets targeting ambiguity can be solved by a native speaker with relative
ease. We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is …

Although the development of AI in games is remarkable, intelligent machines still lag behind
humans in games that require the ability of language understanding. In this paper, we focus …

Both politicians and citizens are increasingly embracing social media as a means to
disseminate information and comment on various topics, particularly during significant …

Solving crossword puzzles requires diverse reasoning capabilities, access to a vast amount
of knowledge about language and the world, and the ability to satisfy the constraints …