Search Results for author: Jakob Grue Simonsen

Found 24 papers, 13 papers with code

Principled Multi-Aspect Evaluation Measures of Rankings

1 code implementation1 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.

Document Ranking Information Retrieval +1

Fact Checking with Insufficient Evidence

no code implementations5 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.

Data Augmentation Fact Checking +2

Diagnostics-Guided Explanation Generation

no code implementations8 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.

Explanation Generation Sentence

Unsupervised Multi-Index Semantic Hashing

1 code implementation26 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.

Information Retrieval Retrieval

Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering

1 code implementation26 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.

Collaborative Filtering

On Position Embeddings in BERT

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.

General Classification Position +1

Multi-Head Self-Attention with Role-Guided Masks

1 code implementation22 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.

Machine Translation text-classification +2

Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19

no code implementations25 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.

Retrieval Text Retrieval

A Diagnostic Study of Explainability Techniques for Text Classification

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.

General Classification text-classification +1

Unsupervised Semantic Hashing with Pairwise Reconstruction

1 code implementation1 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.

Semantic Similarity Semantic Textual Similarity

Factuality Checking in News Headlines with Eye Tracking

1 code implementation17 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.

Content-aware Neural Hashing for Cold-start Recommendation

1 code implementation31 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.

Collaborative Filtering Recommendation Systems

Generating Fact Checking Explanations

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.

Fact Checking Informativeness

Encoding word order in complex embeddings

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.

Language Modelling Machine Translation +5

Variational Hashing-based Collaborative Filtering with Self-Masking

no code implementations25 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.

Collaborative Filtering

Unsupervised Neural Generative Semantic Hashing

no code implementations3 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.

Code Generation Document Ranking +2

Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking

no code implementations20 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.

Fact Checking Misinformation +1

Modelling Sequential Music Track Skips using a Multi-RNN Approach

1 code implementation20 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.

Sequential skip prediction

Contextual Compositionality Detection with External Knowledge Bases andWord Embeddings

no code implementations20 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.

Neural Speed Reading with Structural-Jump-LSTM

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.

Sentence

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