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Found 41 papers, 15 papers with code
“Politeness, you simpleton!” retorted [MASK]: Masked prediction of literary characters
What is the best way to learn embeddings for entities, and what can be learned from them?
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Events
It is important to be able to acquire the knowledge needed for this understanding, though doing so is challenging.
Adjusting Interpretable Dimensions in Embedding Space with Human Judgments
We combine seed-based vectors with guidance from human ratings of where words fall along a specific dimension, and evaluate on predicting both object properties like size and danger, and the stylistic properties of formality and complexity.
X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs
Aligned paragraphs are sourced from Wikipedia pages in different languages, reflecting real information divergences observed in the wild.
POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events
Knowledge about outcomes is critical for complex event understanding but is hard to acquire.
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
Did they answer? Subjective acts and intents in conversational discourse
Discourse signals are often implicit, leaving it up to the interpreter to draw the required inferences.
Leveraging WordNet Paths for Neural Hypernym Prediction
We formulate the problem of hypernym prediction as a sequence generation task, where the sequences are taxonomy paths in WordNet.
When is a bishop not like a rook? When it's like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationships
This paper investigates contextual language models, which produce token representations, as a resource for lexical semantics at the word or type level.
Help! Need Advice on Identifying Advice
Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them.
How to marry a star: probabilistic constraints for meaning in context
In this paper, we derive a notion of 'word meaning in context' that characterizes meaning as both intensional and conceptual.
Narrative Interpolation for Generating and Understanding Stories
We propose a method for controlled narrative/story generation where we are able to guide the model to produce coherent narratives with user-specified target endings by interpolation: for example, we are told that Jim went hiking and at the end Jim needed to be rescued, and we want the model to incrementally generate steps along the way.
Attending to Entities for Better Text Understanding
Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.)
Query-Focused Scenario Construction
The news coverage of events often contains not one but multiple incompatible accounts of what happened.
Evaluating Discourse in Structured Text Representations
Discourse structure is integral to understanding a text and is helpful in many NLP tasks.
From News to Medical: Cross-domain Discourse Segmentation
The first step in discourse analysis involves dividing a text into segments.
Implicit Argument Prediction as Reading Comprehension
Implicit arguments, which cannot be detected solely through syntactic cues, make it harder to extract predicate-argument tuples.
Picking Apart Story Salads
During natural disasters and conflicts, information about what happened is often confusing, messy, and distributed across many sources.
Deep Neural Models of Semantic Shift
This evaluation quantitatively measures how well a model captures the semantic trajectory of a word over time.
Modeling Semantic Plausibility by Injecting World Knowledge
Distributional data tells us that a man can swallow candy, but not that a man can swallow a paintball, since this is never attested.
Implicit Argument Prediction with Event Knowledge
Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract.
Distributional Modeling on a Diet: One-shot Word Learning from Text Only
We test whether distributional models can do one-shot learning of definitional properties from text only.
Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment
We consider the task of predicting lexical entailment using distributional vectors.
Representing Meaning with a Combination of Logical and Distributional Models
In this paper, we focus on the three components of a practical system integrating logical and distributional models: 1) Parsing and task representation is the logic-based part where input problems are represented in probabilistic logic.
