1,186 research outputs found
Discriminative Sentence Modeling for Story Ending Prediction
Story Ending Prediction is a task that needs to select an appropriate ending
for the given story, which requires the machine to understand the story and
sometimes needs commonsense knowledge. To tackle this task, we propose a new
neural network called Diff-Net for better modeling the differences of each
ending in this task. The proposed model could discriminate two endings in three
semantic levels: contextual representation, story-aware representation, and
discriminative representation. Experimental results on the Story Cloze Test
dataset show that the proposed model siginificantly outperforms various systems
by a large margin, and detailed ablation studies are given for better
understanding our model. We also carefully examine the traditional and
BERT-based models on both SCT v1.0 and v1.5 with interesting findings that may
potentially help future studies.Comment: 8 pages, accepted as a conference paper at AAAI 202
Incorporating Structured Commonsense Knowledge in Story Completion
The ability to select an appropriate story ending is the first step towards
perfect narrative comprehension. Story ending prediction requires not only the
explicit clues within the context, but also the implicit knowledge (such as
commonsense) to construct a reasonable and consistent story. However, most
previous approaches do not explicitly use background commonsense knowledge. We
present a neural story ending selection model that integrates three types of
information: narrative sequence, sentiment evolution and commonsense knowledge.
Experiments show that our model outperforms state-of-the-art approaches on a
public dataset, ROCStory Cloze Task , and the performance gain from adding the
additional commonsense knowledge is significant
Domain Adaptation for Statistical Classifiers
The most basic assumption used in statistical learning theory is that
training data and test data are drawn from the same underlying distribution.
Unfortunately, in many applications, the "in-domain" test data is drawn from a
distribution that is related, but not identical, to the "out-of-domain"
distribution of the training data. We consider the common case in which labeled
out-of-domain data is plentiful, but labeled in-domain data is scarce. We
introduce a statistical formulation of this problem in terms of a simple
mixture model and present an instantiation of this framework to maximum entropy
classifiers and their linear chain counterparts. We present efficient inference
algorithms for this special case based on the technique of conditional
expectation maximization. Our experimental results show that our approach leads
to improved performance on three real world tasks on four different data sets
from the natural language processing domain
Transferring Procedural Knowledge across Commonsense Tasks
Stories about everyday situations are an essential part of human
communication, motivating the need to develop AI agents that can reliably
understand these stories. Despite the long list of supervised methods for story
completion and procedural understanding, current AI has no mechanisms to
automatically track and explain procedures in unseen stories. To bridge this
gap, we study the ability of AI models to transfer procedural knowledge to
novel narrative tasks in a transparent manner. We design LEAP: a comprehensive
framework that integrates state-of-the-art modeling architectures, training
regimes, and augmentation strategies based on both natural and synthetic
stories. To address the lack of densely annotated training data, we devise a
robust automatic labeler based on few-shot prompting to enhance the augmented
data. Our experiments with in- and out-of-domain tasks reveal insights into the
interplay of different architectures, training regimes, and augmentation
strategies. LEAP's labeler has a clear positive impact on out-of-domain
datasets, while the resulting dense annotation provides native explainability
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