27 research outputs found
Inferring Interpersonal Relations in Narrative Summaries
Characterizing relationships between people is fundamental for the
understanding of narratives. In this work, we address the problem of inferring
the polarity of relationships between people in narrative summaries. We
formulate the problem as a joint structured prediction for each narrative, and
present a model that combines evidence from linguistic and semantic features,
as well as features based on the structure of the social community in the text.
We also provide a clustering-based approach that can exploit regularities in
narrative types. e.g., learn an affinity for love-triangles in romantic
stories. On a dataset of movie summaries from Wikipedia, our structured models
provide more than a 30% error-reduction over a competitive baseline that
considers pairs of characters in isolation
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
The ability to comprehend wishes or desires and their fulfillment is
important to Natural Language Understanding. This paper introduces the task of
identifying if a desire expressed by a subject in a given short piece of text
was fulfilled. We propose various unstructured and structured models that
capture fulfillment cues such as the subject's emotional state and actions. Our
experiments with two different datasets demonstrate the importance of
understanding the narrative and discourse structure to address this task
Structured Approaches for Exploring Interpersonal Relationships in Natural Language Text
Human relationships have long been studied by scientists from domains like sociology, psychology, literature, etc. for understanding people's desires, goals, actions and expected behaviors. In this dissertation we study inter-personal relationships as expressed in natural language text. Modeling inter-personal relationships from text finds application in general natural language understanding, as well as real-world domains such as social networks, discussion forums, intelligent virtual agents, etc.
We propose that the study of relationships should incorporate not only linguistic cues in text, but also the contexts in which these cues appear. Our investigations, backed by empirical evaluation, support this thesis, and demonstrate that the task benefits from using structured models that incorporate both types of information.
We present such structured models to address the task of modeling the nature of relationships between any two given characters from a narrative. To begin with, we assume that relationships are of two types: cooperative and non-cooperative. We first describe an approach to jointly infer relationships between all characters in the narrative, and demonstrate how the task of characterizing the relationship between two characters can benefit from including information about their relationships with other characters in the narrative. We next formulate the relationship-modeling problem as a sequence prediction task to acknowledge the evolving nature of human relationships, and demonstrate the need to model the history of a relationship in predicting its evolution. Thereafter, we present a data-driven method to automatically discover various types of relationships such as familial, romantic, hostile, etc. Like before, we address the task of modeling evolving relationships but don't restrict ourselves to two types of relationships. We also demonstrate the need to incorporate not only local historical but also global context while solving this problem.
Lastly, we demonstrate a practical application of modeling inter-personal relationships in the domain of online educational discussion forums. Such forums offer opportunities for its users to interact and form deeper relationships. With this view, we address the task of identifying initiation of such deeper relationships between a student and the instructor. Specifically, we analyze contents of the forums to automatically suggest threads to the instructors that require their intervention. By highlighting scenarios that need direct instructor-student interactions, we alleviate the need for the instructor to manually peruse all threads of the forum and also assist students who have limited avenues for communicating with instructors. We do this by incorporating the discourse structure of the thread through latent variables that abstractly represent contents of individual posts and model the flow of information in the thread. Such latent structured models that incorporate the linguistic cues without losing their context can be helpful in other related natural language understanding tasks as well. We demonstrate this by using the model for a very different task: identifying if a stated desire has been fulfilled by the end of a story
Sustaining Fairness via Incremental Learning
Machine learning systems are often deployed for making critical decisions
like credit lending, hiring, etc. While making decisions, such systems often
encode the user's demographic information (like gender, age) in their
intermediate representations. This can lead to decisions that are biased
towards specific demographics. Prior work has focused on debiasing intermediate
representations to ensure fair decisions. However, these approaches fail to
remain fair with changes in the task or demographic distribution. To ensure
fairness in the wild, it is important for a system to adapt to such changes as
it accesses new data in an incremental fashion. In this work, we propose to
address this issue by introducing the problem of learning fair representations
in an incremental learning setting. To this end, we present Fairness-aware
Incremental Representation Learning (FaIRL), a representation learning system
that can sustain fairness while incrementally learning new tasks. FaIRL is able
to achieve fairness and learn new tasks by controlling the rate-distortion
function of the learned representations. Our empirical evaluations show that
FaIRL is able to make fair decisions while achieving high performance on the
target task, outperforming several baselines.Comment: Accepted at AAAI 202
Towards Inter-character Relationship-driven Story Generation
In this paper, we introduce the task of modeling interpersonal relationships
for story generation. For addressing this task, we propose Relationships as
Latent Variables for Story Generation, (ReLiSt). ReLiSt generates stories
sentence by sentence and has two major components - a relationship selector and
a story continuer. The relationship selector specifies a latent variable to
pick the relationship to exhibit in the next sentence and the story continuer
generates the next sentence while expressing the selected relationship in a
coherent way. Our automatic and human evaluations demonstrate that ReLiSt is
able to generate stories with relationships that are more faithful to desired
relationships while maintaining the content quality. The relationship
assignments to sentences during inference bring interpretability to ReLiSt.Comment: EMNLP 202
Unsupervised Opinion Summarization Using Approximate Geodesics
Opinion summarization is the task of creating summaries capturing popular
opinions from user reviews. In this paper, we introduce Geodesic Summarizer
(GeoSumm), a novel system to perform unsupervised extractive opinion
summarization. GeoSumm involves an encoder-decoder based representation
learning model, that generates representations of text as a distribution over
latent semantic units. GeoSumm generates these representations by performing
dictionary learning over pre-trained text representations at multiple decoder
layers. We then use these representations to quantify the relevance of review
sentences using a novel approximate geodesic distance based scoring mechanism.
We use the relevance scores to identify popular opinions in order to compose
general and aspect-specific summaries. Our proposed model, GeoSumm, achieves
state-of-the-art performance on three opinion summarization datasets. We
perform additional experiments to analyze the functioning of our model and
showcase the generalization ability of {\X} across different domains.Comment: Findings of EMNLP 202
Robust Concept Erasure via Kernelized Rate-Distortion Maximization
Distributed representations provide a vector space that captures meaningful
relationships between data instances. The distributed nature of these
representations, however, entangles together multiple attributes or concepts of
data instances (e.g., the topic or sentiment of a text, characteristics of the
author (age, gender, etc), etc). Recent work has proposed the task of concept
erasure, in which rather than making a concept predictable, the goal is to
remove an attribute from distributed representations while retaining other
information from the original representation space as much as possible. In this
paper, we propose a new distance metric learning-based objective, the
Kernelized Rate-Distortion Maximizer (KRaM), for performing concept erasure.
KRaM fits a transformation of representations to match a specified distance
measure (defined by a labeled concept to erase) using a modified
rate-distortion function. Specifically, KRaM's objective function aims to make
instances with similar concept labels dissimilar in the learned representation
space while retaining other information. We find that optimizing KRaM
effectively erases various types of concepts: categorical, continuous, and
vector-valued variables from data representations across diverse domains. We
also provide a theoretical analysis of several properties of KRaM's objective.
To assess the quality of the learned representations, we propose an alignment
score to evaluate their similarity with the original representation space.
Additionally, we conduct experiments to showcase KRaM's efficacy in various
settings, from erasing binary gender variables in word embeddings to
vector-valued variables in GPT-3 representations.Comment: NeurIPS 202
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
Narrative summarization aims to produce a distilled version of a narrative to
describe its most salient events and characters. Summarizing a narrative is
challenging as it requires an understanding of event causality and character
behaviors. To encourage research in this direction, we propose NarraSum, a
large-scale narrative summarization dataset. It contains 122K narrative
documents, which are collected from plot descriptions of movies and TV episodes
with diverse genres, and their corresponding abstractive summaries. Experiments
show that there is a large performance gap between humans and the
state-of-the-art summarization models on NarraSum. We hope that this dataset
will promote future research in summarization, as well as broader studies of
natural language understanding and generation. The dataset is available at
https://github.com/zhaochaocs/narrasum.Comment: EMNLP Findings 202
Affective and Dynamic Beam Search for Story Generation
Storytelling's captivating potential makes it a fascinating research area,
with implications for entertainment, education, therapy, and cognitive studies.
In this paper, we propose Affective Story Generator (AffGen) for generating
interesting narratives. AffGen introduces "intriguing twists" in narratives by
employing two novel techniques-Dynamic Beam Sizing and Affective Reranking.
Dynamic Beam Sizing encourages less predictable, more captivating word choices
using a contextual multi-arm bandit model. Affective Reranking prioritizes
sentence candidates based on affect intensity. Our empirical evaluations, both
automatic and human, demonstrate AffGen's superior performance over existing
baselines in generating affectively charged and interesting narratives. Our
ablation study and analysis provide insights into the strengths and weaknesses
of AffGen.Comment: Accepted at EMNLP-findings 202