24 research outputs found
Dynamic Entity Representations in Neural Language Models
Understanding a long document requires tracking how entities are introduced
and evolve over time. We present a new type of language model, EntityNLM, that
can explicitly model entities, dynamically update their representations, and
contextually generate their mentions. Our model is generative and flexible; it
can model an arbitrary number of entities in context while generating each
entity mention at an arbitrary length. In addition, it can be used for several
different tasks such as language modeling, coreference resolution, and entity
prediction. Experimental results with all these tasks demonstrate that our
model consistently outperforms strong baselines and prior work.Comment: EMNLP 2017 camera-ready versio
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
Generating a reasonable ending for a given story context, i.e., story ending
generation, is a strong indication of story comprehension. This task requires
not only to understand the context clues which play an important role in
planning the plot but also to handle implicit knowledge to make a reasonable,
coherent story.
In this paper, we devise a novel model for story ending generation. The model
adopts an incremental encoding scheme to represent context clues which are
spanning in the story context. In addition, commonsense knowledge is applied
through multi-source attention to facilitate story comprehension, and thus to
help generate coherent and reasonable endings. Through building context clues
and using implicit knowledge, the model is able to produce reasonable story
endings. context clues implied in the post and make the inference based on it.
Automatic and manual evaluation shows that our model can generate more
reasonable story endings than state-of-the-art baselines.Comment: Accepted in AAAI201