1 code implementation • 29 May 2023 • Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, Thomas Lukasiewicz, Jakob Grue Simonsen, Isabelle Augenstein
Explanations of neural models aim to reveal a model's decision-making process for its predictions.
1 code implementation • 1 Dec 2022 • Maria Maistro, Lucas Chaves Lima, Jakob Grue Simonsen, Christina Lioma
Information Retrieval evaluation has traditionally focused on defining principled ways of assessing the relevance of a ranked list of documents with respect to a query.
no code implementations • 5 Apr 2022 • Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, Isabelle Augenstein
To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions.
no code implementations • 8 Sep 2021 • Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, Isabelle Augenstein
When such annotations are not available, explanations are often selected as those portions of the input that maximise a downstream task's performance, which corresponds to optimising an explanation's Faithfulness to a given model.
1 code implementation • 26 Mar 2021 • Christian Hansen, Casper Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
In this work, we propose Multi-Index Semantic Hashing (MISH), an unsupervised hashing model that learns hash codes that are both effective and highly efficient by being optimized for multi-index hashing.
1 code implementation • 26 Mar 2021 • Christian Hansen, Casper Hansen, Jakob Grue Simonsen, Christina Lioma
While this is highly efficient, each bit dimension is equally weighted, which means that potentially discriminative information of the data is lost.
no code implementations • ICLR 2021 • Benyou Wang, Lifeng Shang, Christina Lioma, Xin Jiang, Hao Yang, Qun Liu, Jakob Grue Simonsen
Various Position Embeddings (PEs) have been proposed in Transformer based architectures~(e. g. BERT) to model word order.
1 code implementation • 22 Dec 2020 • Dongsheng Wang, Casper Hansen, Lucas Chaves Lima, Christian Hansen, Maria Maistro, Jakob Grue Simonsen, Christina Lioma
The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms.
no code implementations • 25 Nov 2020 • Lucas Chaves Lima, Casper Hansen, Christian Hansen, Dongsheng Wang, Maria Maistro, Birger Larsen, Jakob Grue Simonsen, Christina Lioma
This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the 2020 TREC-COVID Challenge) organised by the U. S. National Institute of Standards and Technology (NIST) and its Text Retrieval Conference (TREC) division.
1 code implementation • EMNLP 2020 • Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, Isabelle Augenstein
Recent developments in machine learning have introduced models that approach human performance at the cost of increased architectural complexity.
1 code implementation • 1 Jul 2020 • Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
Inspired by this, we present Semantic Hashing with Pairwise Reconstruction (PairRec), which is a discrete variational autoencoder based hashing model.
1 code implementation • 17 Jun 2020 • Christian Hansen, Casper Hansen, Jakob Grue Simonsen, Birger Larsen, Stephen Alstrup, Christina Lioma
We study whether it is possible to infer if a news headline is true or false using only the movement of the human eyes when reading news headlines.
1 code implementation • 31 May 2020 • Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
NeuHash-CF is modelled as an autoencoder architecture, consisting of two joint hashing components for generating user and item hash codes.
no code implementations • ACL 2020 • Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, Isabelle Augenstein
Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims.
1 code implementation • ICLR 2020 • Benyou Wang, Donghao Zhao, Christina Lioma, Qiuchi Li, Peng Zhang, Jakob Grue Simonsen
The benefit of continuous functions over variable positions is that word representations shift smoothly with increasing positions.
no code implementations • 25 Sep 2019 • Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
To this end, we propose an end-to-end trainable variational hashing-based collaborative filtering approach that uses the novel concept of self-masking: the user hash code acts as a mask on the items (using the Boolean AND operation), such that it learns to encode which bits are important to the user, rather than the user's preference towards the underlying item property that the bits represent.
no code implementations • IJCNLP 2019 • Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, Jakob Grue Simonsen
We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification.
1 code implementation • 3 Jun 2019 • Casper Hansen, Christian Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
Word embeddings predict a word from its neighbours by learning small, dense embedding vectors.
no code implementations • 3 Jun 2019 • Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
We present a novel unsupervised generative semantic hashing approach, \textit{Ranking based Semantic Hashing} (RBSH) that consists of both a variational and a ranking based component.
no code implementations • 20 Mar 2019 • Casper Hansen, Christian Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
Automatic fact-checking systems detect misinformation, such as fake news, by (i) selecting check-worthy sentences for fact-checking, (ii) gathering related information to the sentences, and (iii) inferring the factuality of the sentences.
1 code implementation • 20 Mar 2019 • Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
Modelling sequential music skips provides streaming companies the ability to better understand the needs of the user base, resulting in a better user experience by reducing the need to manually skip certain music tracks.
no code implementations • 20 Mar 2019 • Dongsheng Wang, Quichi Li, Lucas Chaves Lima, Jakob Grue Simonsen, Christina Lioma
In this paper, we operationalize the viewpoint that compositionality is contextual rather than deterministic, i. e., that whether a phrase is compositional or non-compositional depends on its context.
1 code implementation • ICLR 2019 • Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference.
no code implementations • 29 Jul 2015 • Casper Petersen, Christina Lioma, Jakob Grue Simonsen, Birger Larsen
We present two novel models of document coherence and their application to information retrieval (IR).