Search Results for author:
Found 44 papers, 16 papers with code
Matching Cultural Heritage items to Wikipedia
Digitised Cultural Heritage (CH) items usually have short descriptions and lack rich contextual information.
A stream computing approach towards scalable NLP
Computational power needs have grown dramatically in recent years.
Studying the Wikipedia Hyperlink Graph for Relatedness and Disambiguation
Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood.
Improving distant supervision using inference learning
Distant supervision is a widely applied approach to automatic training of relation extraction systems and has the advantage that it can generate large amounts of labelled data with minimal effort.
Interoperability of Annotation Schemes: Using the Pepper Framework to Display AWA Documents in the ANNIS Interface
That is often caused by the different linguistic formats used across the applications, which leads to attempts to both establish standard formats to represent linguistic information and to create conversion tools to facilitate this integration.
Two Architectures for Parallel Processing of Huge Amounts of Text
This paper presents two alternative NLP architectures to analyze massive amounts of documents, using parallel processing.
The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD
UKB is an open source collection of programs for performing, among other tasks, knowledge-based Word Sense Disambiguation (WSD).
Evaluating Multimodal Representations on Sentence Similarity: vSTS, Visual Semantic Textual Similarity Dataset
In this paper we introduce vSTS, a new dataset for measuring textual similarity of sentences using multimodal information.
Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation
Named Entity Disambiguation algorithms typically learn a single model for all target entities.
Analyzing the Limitations of Cross-lingual Word Embedding Mappings
Recent research in cross-lingual word embeddings has almost exclusively focused on offline methods, which independently train word embeddings in different languages and map them to a shared space through linear transformations.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+1
Give your Text Representation Models some Love: the Case for Basque
This is suboptimal as, for many languages, the models have been trained on smaller (or lower quality) corpora.
Evaluating Multimodal Representations on Visual Semantic Textual Similarity
In the case of textual representations, inference tasks such as Textual Entailment and Semantic Textual Similarity have been often used to benchmark the quality of textual representations.
Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque
Conversational Question Answering (CQA) systems meet user information needs by having conversations with them, where answers to the questions are retrieved from text.
DoQA -- Accessing Domain-Specific FAQs via Conversational QA
We present DoQA, a dataset with 2, 437 dialogues and 10, 917 QA pairs.
DoQA - Accessing Domain-Specific FAQs via Conversational QA
We present DoQA, a dataset with 2, 437 dialogues and 10, 917 QA pairs.
Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems
In this work, we introduce \emph{Spot The Bot}, a cost-efficient and robust evaluation framework that replaces human-bot conversations with conversations between bots.
Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning
The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility.
Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring
Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+2
Inferring spatial relations from textual descriptions of images
Previous work did not use the caption text information, but a manually provided relation holding between the subject and the object.
Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring
Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+2
Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering
Our results on a visual question answering task which requires external knowledge (OK-VQA) show that our text-only model outperforms pretrained multimodal (image-text) models of comparable number of parameters.
Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources
In recent years, large-scale data collection efforts have prioritized the amount of data collected in order to improve the modeling capabilities of large language models.
Does Corpus Quality Really Matter for Low-Resource Languages?
For instance, 66% of documents are rated as high-quality for EusCrawl, in contrast with <33% for both mC4 and CC100.
PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation
During inference, we build control codes for the desired meter and rhyme scheme, and condition our language model on them to generate formal verse poetry.
Principled Paraphrase Generation with Parallel Corpora
Round-trip Machine Translation (MT) is a popular choice for paraphrase generation, which leverages readily available parallel corpora for supervision.
Noisy Channel for Automatic Text Simplification
In this paper we present a simple re-ranking method for Automatic Sentence Simplification based on the noisy channel scheme.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings.
Do Multilingual Language Models Think Better in English?
In this work, we introduce a new approach called self-translate, which overcomes the need of an external translation system by leveraging the few-shot translation capabilities of multilingual language models.
Common Sense Reasoning
Cross-Lingual Natural Language Inference
+6
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived Dataset
We hypothesize that this is because explicit spatial relations rarely appear in the image captions used to train these models.
Latxa: An Open Language Model and Evaluation Suite for Basque
We introduce Latxa, a family of large language models for Basque ranging from 7 to 70 billion parameters.
XNLIeu: a dataset for cross-lingual NLI in Basque
We have conducted a series of experiments using mono- and multilingual LLMs to assess a) the effect of professional post-edition on the MT system; b) the best cross-lingual strategy for NLI in Basque; and c) whether the choice of the best cross-lingual strategy is influenced by the fact that the dataset is built by translation.
Natural Language Inference
Natural Language Understanding
+1
A Syntax-Aware Edit-based System for Text Simplification
Edit-based text simplification systems have attained much attention in recent years due to their ability to produce simplification solutions that are interpretable, as well as requiring less training examples compared to traditional seq2seq systems.
Ontology Population Reusing Resources for Dialogue Intent Detection: Generic and Multilingual Approach
This work presents a generic semi-automatic strategy to populate the domain ontology of an ontology-driven task-oriented dialogue system, with the aim of performing successful intent detection in the dialogue process, reusing already existing multilingual resources.
BasqueGLUE: A Natural Language Understanding Benchmark for Basque
Natural Language Understanding (NLU) technology has improved significantly over the last few years and multitask benchmarks such as GLUE are key to evaluate this improvement in a robust and general way.
Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature
In this paper we present a quantitative and qualitative analysis of the system.
