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Found 31 papers, 13 papers with code
Choisir le bon co-équipier pour la génération coopérative de texte (Choosing The Right Teammate For Cooperative Text Generation)
Nous évaluons leurs avantages et inconvénients, en explorant leur précision respective sur des tâches de classification, ainsi que leur impact sur la génération coopérative et leur coût de calcul, dans le cadre d’une stratégie de décodage état de l’art, basée sur une recherche arborescente de Monte-Carlo (MCTS).
Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders
With the recent addition of Retrieval-Augmented Generation (RAG), the scope and importance of Information Retrieval (IR) has expanded.
What Makes Multimodal In-Context Learning Work?
Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL) with minimal demonstration examples.
Training Table Question Answering via SQL Query Decomposition
Table Question-Answering involves both understanding the natural language query and grounding it in the context of the input table to extract the relevant information.
Simple Domain Adaptation for Sparse Retrievers
In Information Retrieval, and more generally in Natural Language Processing, adapting models to specific domains is conducted through fine-tuning.
Query Performance Prediction for Neural IR: Are We There Yet?
On top of that, in lexical-oriented scenarios, QPPs fail to predict performance for neural IR systems on those queries where they differ from traditional approaches the most.
LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization
Text Summarization is a popular task and an active area of research for the Natural Language Processing community.
CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval
Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history.
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective
Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture.
Which Discriminator for Cooperative Text Generation?
Language models generate texts by successively predicting probability distributions for next tokens given past ones.
Generative Cooperative Networks for Natural Language Generation
Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation.
Match Your Words! A Study of Lexical Matching in Neural Information Retrieval
Neural Information Retrieval models hold the promise to replace lexical matching models, e. g. BM25, in modern search engines.
SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
Meanwhile, there has been a growing interest in learning \emph{sparse} representations for documents and queries, that could inherit from the desirable properties of bag-of-words models such as the exact matching of terms and the efficiency of inverted indexes.
Ranked #5 on
Zero-shot Text Search
on BEIR
Skim-Attention: Learning to Focus via Document Layout
Motivated by human reading strategies, this paper presents Skim-Attention, a new attention mechanism that takes advantage of the structure of the document and its layout.
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking
In neural Information Retrieval, ongoing research is directed towards improving the first retriever in ranking pipelines.
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods.
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation
QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions.
QuestEval: Summarization Asks for Fact-based Evaluation
Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments.
A White Box Analysis of ColBERT
Transformer-based models are nowadays state-of-the-art in ad-hoc Information Retrieval, but their behavior is far from being understood.
Transductive Zero-Shot Learning using Cross-Modal CycleGAN
We show the efficiency of our Cross-Modal CycleGAN model (CM-GAN) on the ImageNet T-ZSL task where we obtain state-of-the-art results.
ColdGANs: Taming Language GANs with Cautious Sampling Strategies
Training regimes based on Maximum Likelihood Estimation (MLE) suffer from known limitations, often leading to poorly generated text sequences.
MLSUM: The Multilingual Summarization Corpus
We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
Discriminative Adversarial Search for Abstractive Summarization
We introduce a novel approach for sequence decoding, Discriminative Adversarial Search (DAS), which has the desirable properties of alleviating the effects of exposure bias without requiring external metrics.
Incorporating Visual Semantics into Sentence Representations within a Grounded Space
To overcome this limitation, we propose to transfer visual information to textual representations by learning an intermediate representation space: the grounded space.
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models
Abstractive summarization approaches based on Reinforcement Learning (RL) have recently been proposed to overcome classical likelihood maximization.
Self-Attention Architectures for Answer-Agnostic Neural Question Generation
Neural architectures based on self-attention, such as Transformers, recently attracted interest from the research community, and obtained significant improvements over the state of the art in several tasks.
Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses
Unsupervised relation extraction aims at extracting relations between entities in text.
Context-Aware Zero-Shot Learning for Object Recognition
Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging auxiliary knowledge, such as semantic representations.
Learning Multi-Modal Word Representation Grounded in Visual Context
Representing the semantics of words is a long-standing problem for the natural language processing community.
Efficient Document Indexing Using Pivot Tree
We present a novel method for efficiently searching top-k neighbors for documents represented in high dimensional space of terms based on the cosine similarity.
Parameterized Neural Network Language Models for Information Retrieval
Although they present good abilities to cope with both term dependencies and vocabulary mismatch problems, thanks to the distributed representation of words they are based upon, such models could not be used readily in IR, where the estimation of one language model per document (or query) is required.
