Search Results for author: Marianna Apidianaki

Found 57 papers, 15 papers with code

Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases

no code implementations NAACL 2022 Qing Lyu, Zheng Hua, Daoxin Li, Li Zhang, Marianna Apidianaki, Chris Callison-Burch

We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.

Common Sense Reasoning Natural Language Inference

ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns’ Semantic Properties and their Prototypicality

1 code implementation EMNLP (BlackboxNLP) 2021 Marianna Apidianaki, Aina Garí Soler

Large scale language models encode rich commonsense knowledge acquired through exposure to massive data during pre-training, but their understanding of entities and their semantic properties is unclear.

SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes

no code implementations12 Mar 2024 Timothee Mickus, Elaine Zosa, Raúl Vázquez, Teemu Vahtola, Jörg Tiedemann, Vincent Segonne, Alessandro Raganato, Marianna Apidianaki

This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations: outputs from natural language generation (NLG) systems that are fluent, yet inaccurate.

Machine Translation Paraphrase Generation

Calibrating Large Language Models with Sample Consistency

no code implementations21 Feb 2024 Qing Lyu, Kumar Shridhar, Chaitanya Malaviya, Li Zhang, Yanai Elazar, Niket Tandon, Marianna Apidianaki, Mrinmaya Sachan, Chris Callison-Burch

Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application.

Representation Of Lexical Stylistic Features In Language Models' Embedding Space

no code implementations29 May 2023 Qing Lyu, Marianna Apidianaki, Chris Callison-Burch

The representation space of pretrained Language Models (LMs) encodes rich information about words and their relationships (e. g., similarity, hypernymy, polysemy) as well as abstract semantic notions (e. g., intensity).

I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors

1 code implementation24 May 2023 Tuhin Chakrabarty, Arkadiy Saakyan, Olivia Winn, Artemis Panagopoulou, Yue Yang, Marianna Apidianaki, Smaranda Muresan

We propose to solve the task through the collaboration between Large Language Models (LLMs) and Diffusion Models: Instruct GPT-3 (davinci-002) with Chain-of-Thought prompting generates text that represents a visual elaboration of the linguistic metaphor containing the implicit meaning and relevant objects, which is then used as input to the diffusion-based text-to-image models. Using a human-AI collaboration framework, where humans interact both with the LLM and the top-performing diffusion model, we create a high-quality dataset containing 6, 476 visual metaphors for 1, 540 linguistic metaphors and their associated visual elaborations.

Visual Entailment

Explanation-based Finetuning Makes Models More Robust to Spurious Cues

1 code implementation8 May 2023 Josh Magnus Ludan, Yixuan Meng, Tai Nguyen, Saurabh Shah, Qing Lyu, Marianna Apidianaki, Chris Callison-Burch

Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data.

Faithful Chain-of-Thought Reasoning

1 code implementation31 Jan 2023 Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch

While Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka.

Math Multi-hop Question Answering +1

Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction

1 code implementation24 Oct 2022 Yue Yang, Artemis Panagopoulou, Marianna Apidianaki, Mark Yatskar, Chris Callison-Burch

We propose to extract these properties from images and use them in an ensemble model, in order to complement the information that is extracted from language models.

Property Prediction

Towards Faithful Model Explanation in NLP: A Survey

no code implementations22 Sep 2022 Qing Lyu, Marianna Apidianaki, Chris Callison-Burch

In this survey, we review over 110 model explanation methods in NLP through the lens of faithfulness.

counterfactual

Is "My Favorite New Movie" My Favorite Movie? Probing the Understanding of Recursive Noun Phrases

1 code implementation15 Dec 2021 Qing Lyu, Hua Zheng, Daoxin Li, Li Zhang, Marianna Apidianaki, Chris Callison-Burch

We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.

Common Sense Reasoning Natural Language Inference

ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns' Semantic Properties and their Prototypicality

no code implementations12 Oct 2021 Marianna Apidianaki, Aina Garí Soler

Large scale language models encode rich commonsense knowledge acquired through exposure to massive data during pre-training, but their understanding of entities and their semantic properties is unclear.

Scalar Adjective Identification and Multilingual Ranking

no code implementations NAACL 2021 Aina Garí Soler, Marianna Apidianaki

The intensity relationship that holds between scalar adjectives (e. g., nice < great < wonderful) is highly relevant for natural language inference and common-sense reasoning.

Binary Classification Common Sense Reasoning +1

Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Senses

1 code implementation29 Apr 2021 Aina Garí Soler, Marianna Apidianaki

Pre-trained language models (LMs) encode rich information about linguistic structure but their knowledge about lexical polysemy remains unclear.

NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model Performance

1 code implementation NoDaLiDa 2021 Aarne Talman, Marianna Apidianaki, Stergios Chatzikyriakidis, Jörg Tiedemann

We propose a new diagnostics test suite which allows to assess whether a dataset constitutes a good testbed for evaluating the models' meaning understanding capabilities.

Natural Language Inference Sentence

Simple-QE: Better Automatic Quality Estimation for Text Simplification

no code implementations22 Dec 2020 Reno Kriz, Marianna Apidianaki, Chris Callison-Burch

Text simplification systems generate versions of texts that are easier to understand for a broader audience.

Text Simplification

SumQE: a BERT-based Summary Quality Estimation Model

1 code implementation2 Sep 2019 Stratos Xenouleas, Prodromos Malakasiotis, Marianna Apidianaki, Ion Androutsopoulos

We propose SumQE, a novel Quality Estimation model for summarization based on BERT.

A Comparison of Context-sensitive Models for Lexical Substitution

no code implementations WS 2019 Aina Gar{\'\i} Soler, Anne Cocos, Marianna Apidianaki, Chris Callison-Burch

Word embedding representations provide good estimates of word meaning and give state-of-the art performance in semantic tasks.

Word Embeddings

Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package

1 code implementation EMNLP 2018 Ajay Patel, Alexander Sands, Chris Callison-Burch, Marianna Apidianaki

Vector space embedding models like word2vec, GloVe, fastText, and ELMo are extremely popular representations in natural language processing (NLP) applications.

Word Embeddings

Comparing Constraints for Taxonomic Organization

no code implementations NAACL 2018 Anne Cocos, Marianna Apidianaki, Chris Callison-Burch

In this paper, we present a head-to-head comparison of six taxonomic organization algorithms that vary with respect to their structural and transitivity constraints, and treatment of synonymy.

Entity Extraction using GAN

Simplification Using Paraphrases and Context-Based Lexical Substitution

no code implementations NAACL 2018 Reno Kriz, Eleni Miltsakaki, Marianna Apidianaki, Chris Callison-Burch

Lexical simplification involves identifying complex words or phrases that need to be simplified, and recommending simpler meaning-preserving substitutes that can be more easily understood.

Complex Word Identification Lexical Simplification +1

Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation

no code implementations NAACL 2018 Marianna Apidianaki, Guillaume Wisniewski, Anne Cocos, Chris Callison-Burch

We propose a variant of a well-known machine translation (MT) evaluation metric, HyTER (Dreyer and Marcu, 2012), which exploits reference translations enriched with meaning equivalent expressions.

Machine Translation Translation

Mapping the Paraphrase Database to WordNet

no code implementations SEMEVAL 2017 Anne Cocos, Marianna Apidianaki, Chris Callison-Burch

WordNet has facilitated important research in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage.

Clustering

Word Sense Filtering Improves Embedding-Based Lexical Substitution

no code implementations WS 2017 Anne Cocos, Marianna Apidianaki, Chris Callison-Burch

The role of word sense disambiguation in lexical substitution has been questioned due to the high performance of vector space models which propose good substitutes without explicitly accounting for sense.

Clustering Entity Extraction using GAN +5

Lecture bilingue augment\'ee par des alignements multi-niveaux (Augmenting bilingual reading with alignment information)

no code implementations JEPTALNRECITAL 2016 Fran{\c{c}}ois Yvon, Yong Xu, Marianna Apidianaki, Cl{\'e}ment Pillias, Cubaud Pierre

Le travail qui a conduit {\`a} cette d{\'e}monstration combine des outils de traitement des langues multilingues, en particulier l{'}alignement automatique, avec des techniques de visualisation et d{'}interaction.

Semantic Clustering of Pivot Paraphrases

no code implementations LREC 2014 Marianna Apidianaki, Emilia Verzeni, Diana McCarthy

Paraphrases extracted from parallel corpora by the pivot method (Bannard and Callison-Burch, 2005) constitute a valuable resource for multilingual NLP applications.

Clustering Machine Translation +1

Applying cross-lingual WSD to wordnet development

no code implementations LREC 2012 Marianna Apidianaki, Beno{\^\i}t Sagot

The automatic development of semantic resources constitutes an important challenge in the NLP community.

Word Sense Induction

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