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Found 28 papers, 10 papers with code
A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs
Negation is one of the most fundamental concepts in human cognition and language, and several natural language inference (NLI) probes have been designed to investigate pretrained language models’ ability to detect and reason with negation.
MuLan: A Study of Fact Mutability in Language Models
Facts are subject to contingencies and can be true or false in different circumstances.
Multimodal Large Language Models to Support Real-World Fact-Checking
To the best of our knowledge, we are the first to evaluate MLLMs for real-world fact-checking.
Answerability in Retrieval-Augmented Open-Domain Question Answering
To address this limitation, we discovered an efficient approach for training models to recognize such excerpts.
Cultural Adaptation of Recipes
We introduce a new task involving the translation and cultural adaptation of recipes between Chinese and English-speaking cuisines.
Structural Similarities Between Language Models and Neural Response Measurements
Human language processing is also opaque, but neural response measurements can provide (noisy) recordings of activation during listening or reading, from which we can extract similar representations of words and phrases.
Retrieval-augmented Multi-label Text Classification
Multi-label text classification (MLC) is a challenging task in settings of large label sets, where label support follows a Zipfian distribution.
Multi Label Text Classification
Multi-Label Text Classification
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An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text
Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks.
Multi-Label Classification
Multi Label Text Classification
+2
Implications of the Convergence of Language and Vision Model Geometries
Large-scale pretrained language models (LMs) are said to ``lack the ability to connect [their] utterances to the world'' (Bender and Koller, 2020).
SmallCap: Lightweight Image Captioning Prompted with Retrieval Augmentation
Recent advances in image captioning have focused on scaling the data and model size, substantially increasing the cost of pre-training and finetuning.
Dynamic Forecasting of Conversation Derailment
This approach shows mixed results: in a high-quality data setting, a longer average forecast horizon can be achieved at the cost of a small drop in F1; in a low-quality data setting, however, dynamic training propagates the noise and is highly detrimental to performance.
John praised Mary because he? Implicit Causality Bias and Its Interaction with Explicit Cues in LMs
We hypothesize that the temporary challenge humans face in integrating the two contradicting signals, one from the lexical semantics of the verb, one from the sentence-level semantics, would be reflected in higher error rates for models on tasks dependent on causal links.
The ApposCorpus: A new multilingual, multi-domain dataset for factual appositive generation
News articles, image captions, product reviews and many other texts mention people and organizations whose name recognition could vary for different audiences.
PuzzLing Machines: A Challenge on Learning From Small Data
To expose this problem in a new light, we introduce a challenge on learning from small data, PuzzLing Machines, which consists of Rosetta Stone puzzles from Linguistic Olympiads for high school students.
Comparing Unsupervised Word Translation Methods Step by Step
Cross-lingual word vector space alignment is the task of mapping the vocabularies of two languages into a shared semantic space, which can be used for dictionary induction, unsupervised machine translation, and transfer learning.
Adversarial Removal of Demographic Attributes Revisited
Elazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier on a held-out subsample of the data it was trained on.
How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?
Many natural languages assign grammatical gender also to inanimate nouns in the language.
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Induction
We study the composition and quality of the test sets for five diverse languages from this dataset, with concerning findings: (1) a quarter of the data consists of proper nouns, which can be hardly indicative of BDI performance, and (2) there are pervasive gaps in the gold-standard targets.
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages
Parsers are available for only a handful of the world's languages, since they require lots of training data.
Uncovering Probabilistic Implications in Typological Knowledge Bases
The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions.
A Probabilistic Generative Model of Linguistic Typology
In the principles-and-parameters framework, the structural features of languages depend on parameters that may be toggled on or off, with a single parameter often dictating the status of multiple features.
`Indicatements' that character language models learn English morpho-syntactic units and regularities
Character language models have access to surface morphological patterns, but it is not clear whether or \textit{how} they learn abstract morphological regularities.
Copenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding
This paper documents the Team Copenhagen system which placed first in the CoNLL--SIGMORPHON 2018 shared task on universal morphological reinflection, Task 2 with an overall accuracy of 49. 87.
Why is unsupervised alignment of English embeddings from different algorithms so hard?
This paper presents a challenge to the community: Generative adversarial networks (GANs) can perfectly align independent English word embeddings induced using the same algorithm, based on distributional information alone; but fails to do so, for two different embeddings algorithms.
Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction
Most recent approaches to bilingual dictionary induction find a linear alignment between the word vector spaces of two languages.
Indicatements that character language models learn English morpho-syntactic units and regularities
Character language models have access to surface morphological patterns, but it is not clear whether or how they learn abstract morphological regularities.
A Discriminative Latent-Variable Model for Bilingual Lexicon Induction
We introduce a novel discriminative latent variable model for bilingual lexicon induction.
