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Found 46 papers, 32 papers with code
A Long Hard Look at MWEs in the Age of Language Models
In recent years, language models (LMs) have become almost synonymous with NLP.
Good Night at 4 pm?! Time Expressions in Different Cultures
We propose 3 language-agnostic methods, one of which achieves promising results on gold standard annotations that we collected for a small number of languages.
Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right
Large language models have shown promising results in zero-shot settings.
``You are grounded!'': Latent Name Artifacts in Pre-trained Language Models
Pre-trained language models (LMs) may perpetuate biases originating in their training corpus to downstream models.
COMET-M: Reasoning about Multiple Events in Complex Sentences
We propose COMET-M (Multi-Event), an event-centric commonsense model capable of generating commonsense inferences for a target event within a complex sentence.
Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models
The escalating debate on AI's capabilities warrants developing reliable metrics to assess machine "intelligence".
MemeCap: A Dataset for Captioning and Interpreting Memes
Memes are a widely popular tool for web users to express their thoughts using visual metaphors.
From chocolate bunny to chocolate crocodile: Do Language Models Understand Noun Compounds?
Noun compound interpretation is the task of expressing a noun compound (e. g. chocolate bunny) in a free-text paraphrase that makes the relationship between the constituent nouns explicit (e. g. bunny-shaped chocolate).
What happens before and after: Multi-Event Commonsense in Event Coreference Resolution
Event coreference models cluster event mentions pertaining to the same real-world event.
VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge
In contrast to previous methods which inject knowledge from static knowledge bases, we investigate the incorporation of contextualized knowledge using Commonsense Transformer (COMET), an existing knowledge model trained on human-curated knowledge bases.
Ranked #5 on
Visual Question Answering (VQA)
on A-OKVQA
(DA VQA Score metric)
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference
Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation.
It's not Rocket Science : Interpreting Figurative Language in Narratives
Figurative language is ubiquitous in English.
Teach the Rules, Provide the Facts: Targeted Relational-knowledge Enhancement for Textual Inference
Our approach facilitates learning generic inference patterns requiring relational knowledge (e. g. inferences related to hypernymy) during training, while injecting on-demand the relevant relational facts (e. g. pangolin is an animal) at test time.
Surface Form Competition: Why the Highest Probability Answer Isn't Always Right
Large language models have shown promising results in zero-shot settings (Brown et al., 2020; Radford et al., 2019).
Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
In this paper, we investigate the extent to which neural models can reason about natural language rationales that explain model predictions, relying only on distant supervision with no additional annotation cost for human-written rationales.
Do Neural Language Models Overcome Reporting Bias?
Mining commonsense knowledge from corpora suffers from reporting bias, over-representing the rare at the expense of the trivial (Gordon and Van Durme, 2013).
Social Chemistry 101: Learning to Reason about Social and Moral Norms
We present Social Chemistry, a new conceptual formalism to study people's everyday social norms and moral judgments over a rich spectrum of real life situations described in natural language.
Thinking Like a Skeptic: Defeasible Inference in Natural Language
Defeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is a penguin).
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning
Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future.
Paragraph-level Commonsense Transformers with Recurrent Memory
Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text.
Commonsense Reasoning for Natural Language Processing
We organize this tutorial to provide researchers with the critical foundations and recent advances in commonsense representation and reasoning, in the hopes of casting a brighter light on this promising area of future research.
Paraphrasing vs Coreferring: Two Sides of the Same Coin
We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution.
Unsupervised Commonsense Question Answering with Self-Talk
Natural language understanding involves reading between the lines with implicit background knowledge.
"You are grounded!": Latent Name Artifacts in Pre-trained Language Models
Pre-trained language models (LMs) may perpetuate biases originating in their training corpus to downstream models.
Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets
Phenomenon-specific "adversarial" datasets have been recently designed to perform targeted stress-tests for particular inference types.
A Systematic Comparison of English Noun Compound Representations
Building meaningful representations of noun compounds is not trivial since many of them scarcely appear in the corpus.
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution
Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model's success.
coreference-resolution
Cross Document Coreference Resolution
+3
Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition
Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings.
Evaluating Text GANs as Language Models
Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of ``exposure bias''.
SemEval-2018 Task 9: Hypernym Discovery
This paper describes the SemEval 2018 Shared Task on Hypernym Discovery.
Olive Oil is Made \textitof Olives, Baby Oil is Made \textitfor Babies: Interpreting Noun Compounds Using Paraphrases in a Neural Model
Automatic interpretation of the relation between the constituents of a noun compound, e. g. olive oil (source) and baby oil (purpose) is an important task for many NLP applications.
Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations
Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications.
Breaking NLI Systems with Sentences that Require Simple Lexical Inferences
We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge.
Integrating Multiplicative Features into Supervised Distributional Methods for Lexical Entailment
Supervised distributional methods are applied successfully in lexical entailment, but recent work questioned whether these methods actually learn a relation between two words.
Olive Oil is Made of Olives, Baby Oil is Made for Babies: Interpreting Noun Compounds using Paraphrases in a Neural Model
Automatic interpretation of the relation between the constituents of a noun compound, e. g. olive oil (source) and baby oil (purpose) is an important task for many NLP applications.
Acquiring Predicate Paraphrases from News Tweets
We present a simple method for ever-growing extraction of predicate paraphrases from news headlines in Twitter.
Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network
Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems.
A Consolidated Open Knowledge Representation for Multiple Texts
We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution.
Ranked #7 on
Hypernym Discovery
on Music domain
CogALex-V Shared Task: LexNET - Integrated Path-based and Distributional Method for the Identification of Semantic Relations
The reported results in the shared task bring this submission to the third place on subtask 1 (word relatedness), and the first place on subtask 2 (semantic relation classification), demonstrating the utility of integrating the complementary path-based and distributional information sources in recognizing concrete semantic relations.
Path-based vs. Distributional Information in Recognizing Lexical Semantic Relations
Recognizing various semantic relations between terms is beneficial for many NLP tasks.
Improving Hypernymy Detection with an Integrated Path-based and Distributional Method
Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complementary approaches.
