  Most language modeling methods rely on large-scale data to statistically
learn the sequential patterns of words. In this paper, we argue that words are
atomic language units but not necessarily atomic semantic units. Inspired by
HowNet, we use sememes, the minimum semantic units in human languages, to
represent the implicit semantics behind words for language modeling, named
Sememe-Driven Language Model (SDLM). More specifically, to predict the next
word, SDLM first estimates the sememe distribution gave textual context.
Afterward, it regards each sememe as a distinct semantic expert, and these
experts jointly identify the most probable senses and the corresponding word.
In this way, SDLM enables language models to work beyond word-level
manipulation to fine-grained sememe-level semantics and offers us more powerful
tools to fine-tune language models and improve the interpretability as well as
the robustness of language models. Experiments on language modeling and the
downstream application of headline gener- ation demonstrate the significant
effect of SDLM. Source code and data used in the experiments can be accessed at
https:// github.com/thunlp/SDLM-pytorch.
