83 citations · 536 across the 64 of their papers we have counts for
12 papers · 2 filters
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks
Denis Emelin, Ivan Titov, Rico Sennrich
Word sense disambiguation is a well-known source of translation errors in NMT. We posit that some of the incorrect disambiguation choices are due to models' over-reliance on datase…
Fast Interleaved Bidirectional Sequence Generation
Biao Zhang, Ivan Titov, Rico Sennrich
Independence assumptions during sequence generation can speed up inference, but parallel generation of highly inter-dependent tokens comes at a cost in quality. Instead of assuming…
Adaptive Feature Selection for End-to-End Speech Translation
Biao Zhang, Ivan Titov, Barry Haddow +1
Information in speech signals is not evenly distributed, making it an additional challenge for end-to-end (E2E) speech translation (ST) to learn to focus on informative features. I…
A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing
Chunchuan Lyu, Shay B. Cohen, Ivan Titov
Abstract Meaning Representations (AMR) are a broad-coverage semantic formalism which represents sentence meaning as a directed acyclic graph. To train most AMR parsers, one needs t…
Meta-Learning for Domain Generalization in Semantic Parsing
Bailin Wang, Mirella Lapata, Ivan Titov
The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-dom…
Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation
Elena Voita, Rico Sennrich, Ivan Titov
In Neural Machine Translation (and, more generally, conditional language modeling), the generation of a target token is influenced by two types of context: the source and the prefi…
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov
Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. However, there has been little work on interpreting them, an…
Visually Grounded Compound PCFGs
Yanpeng Zhao, Ivan Titov
Exploiting visual groundings for language understanding has recently been drawing much attention. In this work, we study visually grounded grammar induction and learn a constituenc…
Unsupervised Transfer of Semantic Role Models from Verbal to Nominal Domain
Yanpeng Zhao, Ivan Titov
Semantic role labeling (SRL) is an NLP task involving the assignment of predicate arguments to types, called semantic roles. Though research on SRL has primarily focused on verbal…
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models
Biao Zhang, Ivan Titov, Rico Sennrich
Sequence-to-sequence models usually transfer all encoder outputs to the decoder for generation. In this work, by contrast, we hypothesize that these encoder outputs can be compress…
How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking
Nicola De Cao, Michael Schlichtkrull, Wilker Aziz +1
Attribution methods assess the contribution of inputs to the model prediction. One way to do so is erasure: a subset of inputs is considered irrelevant if it can be removed without…
Information-Theoretic Probing with Minimum Description Length
Elena Voita, Ivan Titov
To measure how well pretrained representations encode some linguistic property, it is common to use accuracy of a probe, i.e. a classifier trained to predict the property from the…