3 research outputs found
Generating Diverse Story Continuations with Controllable Semantics
We propose a simple and effective modeling framework for controlled
generation of multiple, diverse outputs. We focus on the setting of generating
the next sentence of a story given its context. As controllable dimensions, we
consider several sentence attributes, including sentiment, length, predicates,
frames, and automatically-induced clusters. Our empirical results demonstrate:
(1) our framework is accurate in terms of generating outputs that match the
target control values; (2) our model yields increased maximum metric scores
compared to standard n-best list generation via beam search; (3) controlling
generation with semantic frames leads to a stronger combination of diversity
and quality than other control variables as measured by automatic metrics. We
also conduct a human evaluation to assess the utility of providing multiple
suggestions for creative writing, demonstrating promising results for the
potential of controllable, diverse generation in a collaborative writing
system.Comment: WNGT 201
THEaiTRE: Artificial Intelligence to Write a Theatre Play
We present THEaiTRE, a starting project aimed at automatic generation of
theatre play scripts. This paper reviews related work and drafts an approach we
intend to follow. We plan to adopt generative neural language models and
hierarchical generation approaches, supported by summarization and machine
translation methods, and complemented with a human-in-the-loop approach.Comment: accepted to AI4Narratives202
Controlling Dialogue Generation with Semantic Exemplars
Dialogue systems pretrained with large language models generate locally
coherent responses, but lack the fine-grained control over responses necessary
to achieve specific goals. A promising method to control response generation is
exemplar-based generation, in which models edit exemplar responses that are
retrieved from training data, or hand-written to strategically address
discourse-level goals, to fit new dialogue contexts. But, current
exemplar-based approaches often excessively copy words from the exemplar
responses, leading to incoherent replies. We present an Exemplar-based Dialogue
Generation model, EDGE, that uses the semantic frames present in exemplar
responses to guide generation. We show that controlling dialogue generation
based on the semantic frames of exemplars, rather than words in the exemplar
itself, improves the coherence of generated responses, while preserving
semantic meaning and conversation goals present in exemplar responses.Comment: Accepted at NAACL 202