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Found 29 papers, 12 papers with code
Annotating Arguments in a Corpus of Opinion Articles
Interest in argument mining has resulted in an increasing number of argument annotated corpora.
Dense Template Retrieval for Customer Support
Templated answers are used extensively in customer support scenarios, providing an efficient way to cover a plethora of topics, with an easily maintainable collection of templates.
Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral 7B Model and Data Augmentation
This paper describes our approach to the SemEval-2024 safe biomedical Natural Language Inference for Clinical Trials (NLI4CT) task, which concerns classifying statements about Clinical Trial Reports (CTRs).
Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval
This study investigates the existence of positional biases in Transformer-based models for text representation learning, particularly in the context of web document retrieval.
Large Language Models for Captioning and Retrieving Remote Sensing Images
In this work, we propose RS-CapRet, a Vision and Language method for remote sensing tasks, in particular image captioning and text-image retrieval.
Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings
Although the International Classification of Diseases (ICD) has been adopted worldwide, manually assigning ICD codes to clinical text is time-consuming, error-prone, and expensive, motivating the development of automated approaches.
Towards a Fully Unsupervised Framework for Intent Induction in Customer Support Dialogues
State of the art models in intent induction require annotated datasets.
Stress Testing BERT Anaphora Resolution Models for Reaction Extraction in Chemical Patents
The high volume of published chemical patents and the importance of a timely acquisition of their information gives rise to automating information extraction from chemical patents.
LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting
Multilingual image captioning has recently been tackled by training with large-scale machine translated data, which is an expensive, noisy, and time-consuming process.
Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformers
The analysis of emotions expressed in text has numerous applications.
Retrieval-augmented Image Captioning
The encoder in our model jointly processes the image and retrieved captions using a pretrained V&L BERT, while the decoder attends to the multimodal encoder representations, benefiting from the extra textual evidence from the retrieved captions.
GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial Intelligence Research
Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption.
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.
Optimization of Artificial Neural Networks models applied to the identification of images of asteroids' resonant arguments
Since the population of asteroids affected by these resonances is, in some cases, of the order of several thousand, this has become a taxing task for a human observer.
State of the Art in Artificial Intelligence applied to the Legal Domain
While Artificial Intelligence applied to the legal domain is a topic with origins in the last century, recent advances in Artificial Intelligence are posed to revolutionize it.
Question rewriting? Assessing its importance for conversational question answering
In conversational question answering, systems must correctly interpret the interconnected interactions and generate knowledgeable answers, which may require the retrieval of relevant information from a background repository.
Retrieval Augmentation for Deep Neural Networks
Despite the use of large training datasets, most models are trained by iterating over single input-output pairs, discarding the remaining examples for the current prediction.
Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval
Our work aimed at experimentally assessing the benefits of model ensembling within the context of neural methods for passage reranking.
Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News
Specifically, we use bi-directional Recurrent Neural Networks, together with max-pooling over the temporal/sequential dimension and neural attention, for representing (i) the headline, (ii) the first two sentences of the news article, and (iii) the entire news article.
Ranked #4 on
Fake News Detection
on FNC-1
Learning to Rank Academic Experts in the DBLP Dataset
More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise.
Using Rank Aggregation for Expert Search in Academic Digital Libraries
The task of expert finding has been getting increasing attention in information retrieval literature.
Learning to Rank for Expert Search in Digital Libraries of Academic Publications
The task of expert finding has been getting increasing attention in information retrieval literature.
