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Found 30 papers, 11 papers with code
War and Pieces: Comparing Perspectives About World War I and II Across Wikipedia Language Communities
Wikipedia is widely used to train models for various tasks including semantic association, text generation, and translation.
Assessing Cognitive Linguistic Influences in the Assignment of Blame
Lab studies in cognition and the psychology of morality have proposed some thematic and linguistic factors that influence moral reasoning.
Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest
Large neural networks can now generate jokes, but do they really "understand" humor?
Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction
We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously.
TGIF: Tree-Graph Integrated-Format Parser for Enhanced UD with Two-Stage Generic- to Individual-Language Finetuning
We present our contribution to the IWPT 2021 shared task on parsing into enhanced Universal Dependencies.
Learning Syntax from Naturally-Occurring Bracketings
Naturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundaries.
On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries
Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches.
Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!
Modeling expressive cross-modal interactions seems crucial in multimodal tasks, such as visual question answering.
Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches
An interesting and frequent type of multi-word expression (MWE) is the headless MWE, for which there are no true internal syntactic dominance relations; examples include many named entities ("Wells Fargo") and dates ("July 5, 2020") as well as certain productive constructions ("blow for blow", "day after day").
Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community
Moderators of online communities often employ comment deletion as a tool.
Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents
Images and text co-occur constantly on the web, but explicit links between images and sentences (or other intra-document textual units) are often not present.
Something's Brewing! Early Prediction of Controversy-causing Posts from Discussion Features
Controversial posts are those that split the preferences of a community, receiving both significant positive and significant negative feedback.
Valency-Augmented Dependency Parsing
We present a complete, automated, and efficient approach for utilizing valency analysis in making dependency parsing decisions.
Global Transition-based Non-projective Dependency Parsing
Shi, Huang, and Lee (2017) obtained state-of-the-art results for English and Chinese dependency parsing by combining dynamic-programming implementations of transition-based dependency parsers with a minimal set of bidirectional LSTM features.
Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers
We generalize Cohen, G\'omez-Rodr\'iguez, and Satta's (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference.
Quantifying the visual concreteness of words and topics in multimodal datasets
Multimodal machine learning algorithms aim to learn visual-textual correspondences.
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set
We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features.
Cats and Captions vs. Creators and the Clock: Comparing Multimodal Content to Context in Predicting Relative Popularity
The content of today's social media is becoming more and more rich, increasingly mixing text, images, videos, and audio.
When confidence and competence collide: Effects on online decision-making discussions
Group discussions are a way for individuals to exchange ideas and arguments in order to reach better decisions than they could on their own.
Talk it up or play it down? (Un)expected correlations between (de-)emphasis and recurrence of discussion points in consequential U.S. economic policy meetings
In meetings where important decisions get made, what items receive more attention may influence the outcome.
Tie-breaker: Using language models to quantify gender bias in sports journalism
Gender bias is an increasingly important issue in sports journalism.
Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions
Changing someone's opinion is arguably one of the most important challenges of social interaction.
All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement
In this paper, we examine three aspects of multi-community engagement: the sequence of communities that users post to, the language that users employ in those communities, and the feedback that users receive, using longitudinal posting behavior on Reddit as our main data source, and DBLP for auxiliary experiments.
The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter
Consider a person trying to spread an important message on a social network.
A Corpus of Sentence-level Revisions in Academic Writing: A Step towards Understanding Statement Strength in Communication
The strength with which a statement is made can have a significant impact on the audience.
You had me at hello: How phrasing affects memorability
Understanding the ways in which information achieves widespread public awareness is a research question of significant interest.
Thumbs up? Sentiment Classification using Machine Learning Techniques
We consider the problem of classifying documents not by topic, but by overall sentiment, e. g., determining whether a review is positive or negative.
