15 research outputs found
What does Attention in Neural Machine Translation Pay Attention to?
Attention in neural machine translation provides the possibility to encode
relevant parts of the source sentence at each translation step. As a result,
attention is considered to be an alignment model as well. However, there is no
work that specifically studies attention and provides analysis of what is being
learned by attention models. Thus, the question still remains that how
attention is similar or different from the traditional alignment. In this
paper, we provide detailed analysis of attention and compare it to traditional
alignment. We answer the question of whether attention is only capable of
modelling translational equivalent or it captures more information. We show
that attention is different from alignment in some cases and is capturing
useful information other than alignments.Comment: To appear in IJCNLP 201
SLUA: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning
Word alignment is essential for the down-streaming cross-lingual language
understanding and generation tasks. Recently, the performance of the neural
word alignment models has exceeded that of statistical models. However, they
heavily rely on sophisticated translation models. In this study, we propose a
super lightweight unsupervised word alignment (SLUA) model, in which
bidirectional symmetric attention trained with a contrastive learning objective
is introduced, and an agreement loss is employed to bind the attention maps,
such that the alignments follow mirror-like symmetry hypothesis. Experimental
results on several public benchmarks demonstrate that our model achieves
competitive, if not better, performance compared to the state of the art in
word alignment while significantly reducing the training and decoding time on
average. Further ablation analysis and case studies show the superiority of our
proposed SLUA. Notably, we recognize our model as a pioneer attempt to unify
bilingual word embedding and word alignments. Encouragingly, our approach
achieves 16.4x speedup against GIZA++, and 50x parameter compression} compared
with the Transformer-based alignment methods. We will release our code to
facilitate the community.Comment: Work in progres
Interrogating the Explanatory Power of Attention in Neural Machine Translation
Attention models have become a crucial component in neural machine
translation (NMT). They are often implicitly or explicitly used to justify the
model's decision in generating a specific token but it has not yet been
rigorously established to what extent attention is a reliable source of
information in NMT. To evaluate the explanatory power of attention for NMT, we
examine the possibility of yielding the same prediction but with counterfactual
attention models that modify crucial aspects of the trained attention model.
Using these counterfactual attention mechanisms we assess the extent to which
they still preserve the generation of function and content words in the
translation process. Compared to a state of the art attention model, our
counterfactual attention models produce 68% of function words and 21% of
content words in our German-English dataset. Our experiments demonstrate that
attention models by themselves cannot reliably explain the decisions made by a
NMT model.Comment: Accepted at the 3rd Workshop on Neural Generation and Translation
(WNGT 2019) held at EMNLP-IJCNLP 2019 (Camera ready
Linear Log-Normal Attention with Unbiased Concentration
Transformer models have achieved remarkable results in a wide range of
applications. However, their scalability is hampered by the quadratic time and
memory complexity of the self-attention mechanism concerning the sequence
length. This limitation poses a substantial obstacle when dealing with long
documents or high-resolution images. In this work, we study the self-attention
mechanism by analyzing the distribution of the attention matrix and its
concentration ability. Furthermore, we propose instruments to measure these
quantities and introduce a novel self-attention mechanism, Linear Log-Normal
Attention, designed to emulate the distribution and concentration behavior of
the original self-attention. Our experimental results on popular natural
language benchmarks reveal that our proposed Linear Log-Normal Attention
outperforms other linearized attention alternatives, offering a promising
avenue for enhancing the scalability of transformer models.Comment: 22 pages, 20 figures, 5 tables, submitted to ICLR202
What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis
End-to-end DNN architectures have pushed the state-of-the-art in speech
technologies, as well as in other spheres of AI, leading researchers to train
more complex and deeper models. These improvements came at the cost of
transparency. DNNs are innately opaque and difficult to interpret. We no longer
understand what features are learned, where they are preserved, and how they
inter-operate. Such an analysis is important for better model understanding,
debugging and to ensure fairness in ethical decision making. In this work, we
analyze the representations trained within deep speech models, towards the task
of speaker recognition, dialect identification and reconstruction of masked
signals. We carry a layer- and neuron-level analysis on the utterance-level
representations captured within pretrained speech models for speaker, language
and channel properties. We study: is this information captured in the learned
representations? where is it preserved? how is it distributed? and can we
identify a minimal subset of network that posses this information. Using
diagnostic classifiers, we answered these questions. Our results reveal: (i)
channel and gender information is omnipresent and is redundantly distributed
(ii) complex properties such as dialectal information is encoded only in the
task-oriented pretrained network and is localised in the upper layers (iii) a
minimal subset of neurons can be extracted to encode the predefined property
(iv) salient neurons are sometimes shared between properties and can highlights
presence of biases in the network. Our cross-architectural comparison indicates
that (v) the pretrained models captures speaker-invariant information and (vi)
the pretrained CNNs models are competitive to the Transformers for encoding
information for the studied properties. To the best of our knowledge, this is
the first study to investigate neuron analysis on the speech models.Comment: Submitted to CSL. Keywords: Speech, Neuron Analysis,
Interpretibility, Diagnostic Classifier, AI explainability, End-to-End
Architectur
An empirical analysis of phrase-based and neural machine translation
Two popular types of machine translation (MT) are phrase-based and neural
machine translation systems. Both of these types of systems are composed of
multiple complex models or layers. Each of these models and layers learns
different linguistic aspects of the source language. However, for some of these
models and layers, it is not clear which linguistic phenomena are learned or
how this information is learned. For phrase-based MT systems, it is often clear
what information is learned by each model, and the question is rather how this
information is learned, especially for its phrase reordering model. For neural
machine translation systems, the situation is even more complex, since for many
cases it is not exactly clear what information is learned and how it is
learned.
To shed light on what linguistic phenomena are captured by MT systems, we
analyze the behavior of important models in both phrase-based and neural MT
systems. We consider phrase reordering models from phrase-based MT systems to
investigate which words from inside of a phrase have the biggest impact on
defining the phrase reordering behavior. Additionally, to contribute to the
interpretability of neural MT systems we study the behavior of the attention
model, which is a key component in neural MT systems and the closest model in
functionality to phrase reordering models in phrase-based systems. The
attention model together with the encoder hidden state representations form the
main components to encode source side linguistic information in neural MT. To
this end, we also analyze the information captured in the encoder hidden state
representations of a neural MT system. We investigate the extent to which
syntactic and lexical-semantic information from the source side is captured by
hidden state representations of different neural MT architectures.Comment: PhD thesis, University of Amsterdam, October 2020.
https://pure.uva.nl/ws/files/51388868/Thesis.pd