44 research outputs found
SupWSD: a flexible toolkit for supervised word sense disambiguation
In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. The source code of SupWSD is available at http://github.com/SI3P/SupWSD
Multilingual NMT with a language-independent attention bridge
In this paper, we propose a multilingual encoder-decoder architecture capable
of obtaining multilingual sentence representations by means of incorporating an
intermediate {\em attention bridge} that is shared across all languages. That
is, we train the model with language-specific encoders and decoders that are
connected via self-attention with a shared layer that we call attention bridge.
This layer exploits the semantics from each language for performing translation
and develops into a language-independent meaning representation that can
efficiently be used for transfer learning. We present a new framework for the
efficient development of multilingual NMT using this model and scheduled
training. We have tested the approach in a systematic way with a multi-parallel
data set. We show that the model achieves substantial improvements over strong
bilingual models and that it also works well for zero-shot translation, which
demonstrates its ability of abstraction and transfer learning
Entity Linking meets Word Sense Disambiguation: A Unified Approach
Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity
of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-ofthe-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http://babelfy.orgEntity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity
of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-ofthe-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http://babelfy.or
New frontiers in supervised word sense disambiguation: building multilingual resources and neural models on a large scale
Word Sense Disambiguation is a long-standing task in Natural Language Processing
(NLP), lying at the core of human language understanding. While it has already
been studied from many different angles over the years, ranging from knowledge
based systems to semi-supervised and fully supervised models, the field seems to
be slowing down in respect to other NLP tasks, e.g., part-of-speech tagging and
dependencies parsing. Despite the organization of several international competitions
aimed at evaluating Word Sense Disambiguation systems, the evaluation of automatic
systems has been problematic mainly due to the lack of a reliable evaluation
framework aiming at performing a direct quantitative confrontation.
To this end we develop a unified evaluation framework and analyze the performance
of various Word Sense Disambiguation systems in a fair setup. The results
show that supervised systems clearly outperform knowledge-based models. Among
the supervised systems, a linear classifier trained on conventional local features
still proves to be a hard baseline to beat. Nonetheless, recent approaches exploiting
neural networks on unlabeled corpora achieve promising results, surpassing this
hard baseline in most test sets. Even though supervised systems tend to perform
best in terms of accuracy, they often lose ground to more flexible knowledge-based
solutions, which do not require training for every disambiguation target. To bridge
this gap we adopt a different perspective and rely on sequence learning to frame
the disambiguation problem: we propose and study in depth a series of end-to-end
neural architectures directly tailored to the task, from bidirectional Long ShortTerm
Memory to encoder-decoder models. Our extensive evaluation over standard
benchmarks and in multiple languages shows that sequence learning enables more
versatile all-words models that consistently lead to state-of-the-art results, even
against models trained with engineered features.
However, supervised systems need annotated training corpora and the few available
to date are of limited size: this is mainly due to the expensive and timeconsuming
process of annotating a wide variety of word senses at a reasonably high
scale, i.e., the so-called knowledge acquisition bottleneck. To address this issue, we
also present different strategies to acquire automatically high quality sense annotated
data in multiple languages, without any manual effort. We assess the quality of the
sense annotations both intrinsically and extrinsically achieving competitive results
on multiple tasks
Personalization in BERT with Adapter Modules and Topic Modelling
As a result of the widespread use of intelligent assistants, personalization in dialogue systems has become
a hot topic in both research and industry. Typically, training such systems is computationally expensive,
especially when using recent large language models. To address this challenge, we develop an approach
to personalize dialogue systems using adapter layers and topic modelling. Our implementation enables
the model to incorporate user-specific information, achieving promising results by training only a small
fraction of parameters
A Systematic Study of Inner-Attention-Based Sentence Representations in Multilingual Neural Machine Translation
Neural machine translation has considerably improved the quality of automatic translations by learning good representations of input sentences. In this article, we explore a multilingual translation model capable of producing fixed-size sentence representations by incorporating an intermediate crosslingual shared layer, which we refer to as attention bridge. This layer exploits the semantics from each language and develops into a language-agnostic meaning representation that can be efficiently used for transfer learning. We systematically study the impact of the size of the attention bridge and the effect of including additional languages in the model. In contrast to related previous work, we demonstrate that there is no conflict between translation performance and the use of sentence representations in downstream tasks. In particular, we show that larger intermediate layers not only improve translation quality, especially for long sentences, but also push the accuracy of trainable classification tasks. Nevertheless, shorter representations lead to increased compression that is beneficial in non-trainable similarity tasks. Similarly, we show that trainable downstream tasks benefit from multilingual models, whereas additional language signals do not improve performance in non-trainable benchmarks. This is an important insight that helps to properly design models for specific applications. Finally, we also include an in-depth analysis of the proposed attention bridge and its ability to encode linguistic properties. We carefully analyze the information that is captured by individual attention heads and identify interesting patterns that explain the performance of specific settings in linguistic probing tasks.Peer reviewe
Recent Trends in Word Sense Disambiguation : A Survey
Survey TrackPeer reviewe
A Large-Scale Multilingual Disambiguation of Glosses
Linking concepts and named entities to knowledge bases has become a crucial Natural Language Understanding task. In this respect, recent works have shown the key advantage of exploiting textual definitions in various Natural Language Processing applications. However, to date there are no reliable large-scale corpora of sense-annotated textual definitions available to the research community. In this paper we present a large-scale high-quality corpus of disambiguated glosses in multiple languages, comprising sense annotations of
both concepts and named entities from a unified sense inventory. Our approach for the construction and disambiguation of the corpus builds upon the structure of a large multilingual semantic network and a state-of-the-art disambiguation system; first, we gather complementary information of equivalent definitions across different languages to provide context for disambiguation, and then we combine it with a semantic similarity-based refinement. As a result we obtain a multilingual corpus of textual definitions featuring over 38 million definitions in 263 languages, and we make it freely available at http://lcl.uniroma1.it/disambiguated-glosses. Experiments on Open Information Extraction and Sense Clustering show how two state-of-the-art approaches improve their performance by integrating our disambiguated corpus into their pipeline