View The Document

Accession Number:

AD1157773

Title:

Affect-LM: A Neural Language Model for Customizable Affective Text Generation

Author(s):

Author Organization(s):

Report Date:

2017-07-01

Abstract:

Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for generating conversational text, conditioned on affect categories. Our proposed model, Affect-LM enables us to customize the degree of emotional content in generated sentences through an additional design parameter. Perception studies conducted using Amazon Mechanical Turk show that Affect-LM generates naturally looking emotional sentences without sacrificing grammatical correctness. Affect-LM also learns affect discriminative word representations, and perplexity experiments show that additional affective information in conversational text can improve language model prediction.

Pages:

9

File Size:

0.65MB

Descriptors:

Identifiers:

SubjectCategory:

Communities of Interest:

Distribution Statement:

Approved For Public Release

View The Document