221 research outputs found
Attention-Based End-to-End Speech Recognition on Voice Search
Recently, there has been a growing interest in end-to-end speech recognition
that directly transcribes speech to text without any predefined alignments. In
this paper, we explore the use of attention-based encoder-decoder model for
Mandarin speech recognition on a voice search task. Previous attempts have
shown that applying attention-based encoder-decoder to Mandarin speech
recognition was quite difficult due to the logographic orthography of Mandarin,
the large vocabulary and the conditional dependency of the attention model. In
this paper, we use character embedding to deal with the large vocabulary.
Several tricks are used for effective model training, including L2
regularization, Gaussian weight noise and frame skipping. We compare two
attention mechanisms and use attention smoothing to cover long context in the
attention model. Taken together, these tricks allow us to finally achieve a
character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% on
the MiTV voice search dataset. While together with a trigram language model,
CER and SER reach 2.81% and 5.77%, respectively
Learning Character-level Compositionality with Visual Features
Previous work has modeled the compositionality of words by creating
character-level models of meaning, reducing problems of sparsity for rare
words. However, in many writing systems compositionality has an effect even on
the character-level: the meaning of a character is derived by the sum of its
parts. In this paper, we model this effect by creating embeddings for
characters based on their visual characteristics, creating an image for the
character and running it through a convolutional neural network to produce a
visual character embedding. Experiments on a text classification task
demonstrate that such model allows for better processing of instances with rare
characters in languages such as Chinese, Japanese, and Korean. Additionally,
qualitative analyses demonstrate that our proposed model learns to focus on the
parts of characters that carry semantic content, resulting in embeddings that
are coherent in visual space.Comment: Accepted to ACL 201
Recommended from our members
Domain adaptation for neural machine translation
The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, such translation models struggle when translating text of a specific domain. A domain may consist of text on a well-defined topic, or text of unknown provenance with an identifiable vocabulary distribution, or language with some other stylometric feature. While NMT models can achieve good translation performance on domain-specific data via simple tuning on a representative training corpus, such data-centric approaches have negative side-effects. These include over-fitting, brittleness, and `catastrophic forgetting' of previous training examples.
In this thesis we instead explore more robust approaches to domain adaptation for NMT. We consider the case where a system is adapted to a specified domain of interest, but may also need to accommodate new language, or domain-mismatched sentences. We explore techniques relating to data selection and curriculum, model parameter adaptation procedure, and inference procedure. We show that iterative fine-tuning can achieve strong performance over multiple related domains, and that Elastic Weight Consolidation can be used to mitigate catastrophic forgetting in NMT domain adaptation across multiple sequential domains. We develop a robust variant of Minimum Risk Training which allows more beneficial use of small, highly domain-specific tuning sets than simple cross-entropy fine-tuning, and can mitigate exposure bias resulting from domain over-fitting. We extend Bayesian Interpolation inference schemes to Neural Machine Translation, allowing adaptive weighting of NMT ensembles to translate text from an unknown domain.
Finally we demonstrate the benefit of multi-domain adaptation approaches for other lines of NMT research. We show that NMT systems using multiple forms of data representation can benefit from multi-domain inference approaches. We also demonstrate a series of domain adaptation approaches to mitigating the effects of gender bias in machine translation
Why don't people use character-level machine translation?
We present a literature and empirical survey that critically assesses the
state of the art in character-level modeling for machine translation (MT).
Despite evidence in the literature that character-level systems are comparable
with subword systems, they are virtually never used in competitive setups in
WMT competitions. We empirically show that even with recent modeling
innovations in character-level natural language processing, character-level MT
systems still struggle to match their subword-based counterparts.
Character-level MT systems show neither better domain robustness, nor better
morphological generalization, despite being often so motivated. However, we are
able to show robustness towards source side noise and that translation quality
does not degrade with increasing beam size at decoding time.Comment: 16 pages, 4 figures; Findings of ACL 2022, camera-read
- …