6 research outputs found
Predicting the Quality of Short Narratives from Social Media
An important and difficult challenge in building computational models for
narratives is the automatic evaluation of narrative quality. Quality evaluation
connects narrative understanding and generation as generation systems need to
evaluate their own products. To circumvent difficulties in acquiring
annotations, we employ upvotes in social media as an approximate measure for
story quality. We collected 54,484 answers from a crowd-powered
question-and-answer website, Quora, and then used active learning to build a
classifier that labeled 28,320 answers as stories. To predict the number of
upvotes without the use of social network features, we create neural networks
that model textual regions and the interdependence among regions, which serve
as strong benchmarks for future research. To our best knowledge, this is the
first large-scale study for automatic evaluation of narrative quality.Comment: 7 pages, 2 figures. Accepted at the 2017 IJCAI conferenc
Understanding Actors and Evaluating Personae with Gaussian Embeddings
Understanding narrative content has become an increasingly popular topic.
Nonetheless, research on identifying common types of narrative characters, or
personae, is impeded by the lack of automatic and broad-coverage evaluation
methods. We argue that computationally modeling actors provides benefits,
including novel evaluation mechanisms for personae. Specifically, we propose
two actor-modeling tasks, cast prediction and versatility ranking, which can
capture complementary aspects of the relation between actors and the characters
they portray. For an actor model, we present a technique for embedding actors,
movies, character roles, genres, and descriptive keywords as Gaussian
distributions and translation vectors, where the Gaussian variance corresponds
to actors' versatility. Empirical results indicate that (1) the technique
considerably outperforms TransE (Bordes et al. 2013) and ablation baselines and
(2) automatically identified persona topics (Bamman, O'Connor, and Smith 2013)
yield statistically significant improvements in both tasks, whereas simplistic
persona descriptors including age and gender perform inconsistently, validating
prior research.Comment: Accepted at AAAI 201
A Study of Question Effectiveness Using Reddit "Ask Me Anything" Threads
Asking effective questions is a powerful social skill. In this paper we seek
to build computational models that learn to discriminate effective questions
from ineffective ones. Armed with such a capability, future advanced systems
can evaluate the quality of questions and provide suggestions for effective
question wording. We create a large-scale, real-world dataset that contains
over 400,000 questions collected from Reddit "Ask Me Anything" threads. Each
thread resembles an online press conference where questions compete with each
other for attention from the host. This dataset enables the development of a
class of computational models for predicting whether a question will be
answered. We develop a new convolutional neural network architecture with
variable-length context and demonstrate the efficacy of the model by comparing
it with state-of-the-art baselines and human judges.Comment: 6 page
Modeling Reportable Events as Turning Points in Narrative
We present novel experiments in model-ing the rise and fall of story characteristics within narrative, leading up to the Most Reportable Event (MRE), the compelling event that is the nucleus of the story. We construct a corpus of personal narratives from the bulletin board website Reddit, using the organization of Reddit content into topic-specific communities to auto-matically identify narratives. Leveraging the structure of Reddit comment threads, we automatically label a large dataset of narratives. We present a change-based model of narrative that tracks changes in formality, affect, and other characteristics over the course of a story, and we use this model in distant supervision and self-training experiments that achieve signifi-cant improvements over the baselines at the task of identifying MREs.