83 citations · 536 across the 64 of their papers we have counts for
16 papers · 2 filters
Unsupervised Opinion Summarization as Copycat-Review Generation
Arthur Bražinskas, Mirella Lapata, Ivan Titov
Opinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews. While the majori…
Context-Aware Monolingual Repair for Neural Machine Translation
Elena Voita, Rico Sennrich, Ivan Titov
Modern sentence-level NMT systems often produce plausible translations of isolated sentences. However, when put in context, these translations may end up being inconsistent with ea…
The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents
Shangmin Guo, Yi Ren, Serhii Havrylov +3
Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics. Recently, with the development of deep learning techniques, researc…
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks
Xinchi Chen, Chunchuan Lyu, Ivan Titov
Semantic role labeling (SRL) involves extracting propositions (i.e. predicates and their typed arguments) from natural language sentences. State-of-the-art SRL models rely on power…
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs
Bailin Wang, Ivan Titov, Mirella Lapata
Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces…
Semantic Role Labeling with Iterative Structure Refinement
Chunchuan Lyu, Shay B. Cohen, Ivan Titov
Modern state-of-the-art Semantic Role Labeling (SRL) methods rely on expressive sentence encoders (e.g., multi-layer LSTMs) but tend to model only local (if any) interactions betwe…
The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives
Elena Voita, Rico Sennrich, Ivan Titov
We seek to understand how the representations of individual tokens and the structure of the learned feature space evolve between layers in deep neural networks under different lear…
Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling
Diego Marcheggiani, Ivan Titov
Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles. Even though most semantic-role formalisms are built upon constit…
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention
Biao Zhang, Ivan Titov, Rico Sennrich
The general trend in NLP is towards increasing model capacity and performance via deeper neural networks. However, simply stacking more layers of the popular Transformer architectu…
Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts
Denis Emelin, Ivan Titov, Rico Sennrich
The transformer is a state-of-the-art neural translation model that uses attention to iteratively refine lexical representations with information drawn from the surrounding context…
Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming
Caio Corro, Ivan Titov
We treat projective dependency trees as latent variables in our probabilistic model and induce them in such a way as to be beneficial for a downstream task, without relying on any…
Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned
Elena Voita, David Talbot, Fedor Moiseev +2
Multi-head self-attention is a key component of the Transformer, a state-of-the-art architecture for neural machine translation. In this work we evaluate the contribution made by i…
Boosting Entity Linking Performance by Leveraging Unlabeled Documents
Phong Le, Ivan Titov
Modern entity linking systems rely on large collections of documents specifically annotated for the task (e.g., AIDA CoNLL). In contrast, we propose an approach which exploits only…
Distant Learning for Entity Linking with Automatic Noise Detection
Phong Le, Ivan Titov
Accurate entity linkers have been produced for domains and languages where annotated data (i.e., texts linked to a knowledge base) is available. However, little progress has been m…
Obfuscation for Privacy-preserving Syntactic Parsing
Zhifeng Hu, Serhii Havrylov, Ivan Titov +1
The goal of homomorphic encryption is to encrypt data such that another party can operate on it without being explicitly exposed to the content of the original data. We introduce a…
Modeling Latent Sentence Structure in Neural Machine Translation
Jasmijn Bastings, Wilker Aziz, Ivan Titov +1
Recently it was shown that linguistic structure predicted by a supervised parser can be beneficial for neural machine translation (NMT). In this work we investigate a more challeng…