Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints
An anagram is a sentence or a phrase that is made by permutating the characters of an input sentence or a phrase. For example, {``}Trims cash{''} is an anagram of {``}Christmas{''}. Existing automatic anagram generation methods can find possible combinations of words form an anagram. However, they do not pay much attention to the naturalness of the generated anagrams. In this paper, we show that simple depth-first search can yield natural anagrams when it is combined with modern neural language models. Human evaluation results show that the proposed method can generate significantly more natural anagrams than baseline methods.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
