United States

We investigate the impact of using Multi-Task Learning (MTL) to predict mood changes over time for each individual (social media user) and also the impact it has on predicting an individual’s suicide risk severity. The presented models were developed as a part of the Computational Linguistics and Clinical Psychology (CLPsych) 2022 shared task.

NAACL 2022

Multi-Task Learning to Capture Changes in Mood Over Time

suicide ideation

reddit

mental health

social media

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

workshop paper

When reading stories, people can naturally identify sentences in which a new event starts, i.e. event boundaries, using their knowledge of how events typically unfold, but a computational model to detect event boundaries is not yet available. We characterize and detect sentences with expected or surprising event boundaries in an annotated corpus of short diary-like stories, using a model that combines commonsense knowledge and narrative flow features with a RoBERTa classifier. Our results show that, while commonsense and narrative features can help improve performance overall, detecting event boundaries that are more subjective remains challenging for our model. We also find that sentences marking surprising event boundaries are less likely to be causally related to the preceding sentence, but are more likely to express emotional reactions of story characters, compared to sentences with no event boundary.

Uncovering Surprising Event Boundaries in Narratives

While large language models have shown exciting progress on several NLP benchmarks, evaluating their ability for complex analogical reasoning remains under-explored. Here, we introduce a high-quality crowdsourced dataset of narratives for employing proverbs in context as a benchmark for abstract language understanding. The dataset provides fine-grained annotation of aligned spans between proverbs and narratives, and contains minimal lexical overlaps between narratives and proverbs, ensuring that models need to go beyond surfacelevel reasoning to succeed. We explore three tasks: (1) proverb recommendation and alignment prediction, (2) narrative generation for a given proverb and topic, and (3) identifying narratives with similar motifs. Our experiments show that neural language models struggle on these tasks compared to humans, and these tasks pose multiple learning challenges.

ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding

Internet forums such as Reddit offer people a platform to ask for advice when they encounter various issues at work, school or in relationships. Telling helpful comments apart from unhelpful comments to these advice-seeking posts can help people and dialogue agents to become more helpful in offering advice. We propose a dataset that contains both helpful and unhelpful comments in response to such requests. We then relate helpfulness to the closely related construct of empathy. Finally, we analyze the language features that are associated with helpful and unhelpful comments.

How to be Helpful on Online Support Forums?

Over the past decade, an extended line of research
developed a broad range of methods for reasoning
about narrative from a social perspective. This
often revolved around transforming literary text
into a character network representation. However,
there remain inconsistent traits of narrative
structure produced computationally by either neural
language technology or network theory tools. In
this paper, we propose an encoder-decoder model
with a main objective to mitigate the apparent
computational divergence. Our encoder novelty lies
in generating hundreds of location-based network
graphs to render a fine-grained narrative. We
further formalize a decoder task for detecting
character communities and analyze modularity and
membership affiliation. Through empirical
experiments, we present visualization of stages
in our computational process for four literary
fiction novels.

Multi-Graph Encoder-Decoder Model for Location-based Character Networks in Literary Narrative

Long-form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions. To better understand this complex and understudied task, we study the functional structure of long-form answers collected from three datasets, ELI5, WebGPT and Natural Questions. Our main goal is to understand how humans organize information to craft complex answers. We develop an ontology of six sentence-level functional roles for long-form answers, and annotate 3.9k sentences in 640 answer paragraphs. Different answer collection methods manifest in different discourse structures. We further analyze model-generated answers – finding that annotators agree less with each other when annotating model-generated answers compared to annotating human-written answers. Our annotated data enables training a strong classifier that can be used for automatic analysis. We hope our work can inspire future research on discourse-level modeling and evaluation of long-form QA systems.

How Do We Answer Complex Questions: Discourse Structure of Long-form Answers

We experiment with adapting generative language
models for the generation of long coherent
narratives in the form of theatre plays.
Since fully automatic generation of whole plays
is not currently feasible, we created an interactive
tool that allows a human user to steer the
generation somewhat while minimizing intervention.
We pursue two approaches to long-text
generation: a flat generation with summarization
of context, and a hierarchical text-to-text
two-stage approach, where a synopsis is generated
first and then used to condition generation
of the final script. Our preliminary results and
discussions with theatre professionals show improvements
over vanilla language model generation,
but also identify important limitations of
our approach.

GPT-2-based Human-in-the-loop Theatre Play Script Generation

We present a novel approach to generating
scripts by using agents with different personality
types. To manage character interaction
in the script, we employ simulated dramatic
networks. Automatic and human evaluation on
multiple criteria shows that our approach outperforms
a vanilla-GPT2-based baseline.

DialogueScript: Using Dialogue Agents to Produce a Script

Decision making theories such as Fuzzy-Trace Theory (FTT) suggest that individuals tend to rely on gist, or bottom-line meaning, in the text when making decisions. In this work, we delineate the process of developing GisPy, an open-source tool in Python for measuring the Gist Inference Score (GIS) in text. Evaluation of GisPy on documents in three benchmarks from the news and scientific text domains demonstrates that scores generated by our tool significantly distinguish low vs. high gist documents. Our tool is publicly available to use at: https://github.com/phosseini/GisPy.

GisPy: A Tool for Measuring Gist Inference Score in Text

When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc.
While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.

''Let Your Characters Tell Their Story'': A Dataset for Character-Centric Narrative Understanding

Story characters not only perform actions, they typically also perceive, feel, think, and communicate. Here we are interested in how children render characters’ perspectives when freely telling a fantasy story. Drawing on a sample of 150 narratives elicited from Dutch children aged 4-12, we provide an inventory of 750 instances of character-perspective representation (CPR), distinguishing fourteen different types. Firstly, we observe that character perspectives are ubiquitous in freely told children’s stories and take more varied forms than traditional frameworks can accommodate. Secondly, we discuss variation in the use of different types of CPR across age groups, finding that character perspectives are being fleshed out in more advanced and diverse ways as children grow older. Thirdly, we explore whether such variation can be meaningfully linked to automatically extracted linguistic features, thereby probing the potential for using automated tools from NLP to extract and classify character perspectives in children’s stories.

Multi-Task Learning to Capture Changes in Mood Over Time

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Uncovering Surprising Event Boundaries in Narratives

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Detecting Suicidality with a Contextual Graph Neural Network

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Multi-Task Learning to Capture Changes in Mood Over Time

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Next from NAACL 2022

Uncovering Surprising Event Boundaries in Narratives

Similar lecture

Detecting Suicidality with a Contextual Graph Neural Network

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads