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Register a new userAvengers, Ensemble! Benefits of ensembling in grapheme-to-phoneme prediction
المنتقمون، فرقة!فوائد الوكالية في التنبؤ في Grapeme-to-Voneme
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نحن تصف ثلاثة أنظمة تفوق خطوط خط أساس للمهمة الفرعية للإنجليزية فقط فقط من المهمة المشتركة Sigmorphon 2021 1: مجموعة صغيرة يستخدم فريق التعرف على الكلام في Dialpad داخليا، نموذجا غير معروف من الرف، وأكبرنموذج الفرقة التي تضم هذه وغيرها.نناقش التحديات المرتبطة بالبيانات المقدمة، جنبا إلى جنب مع خطوات المعالجة التي أخذناها.
We describe three baseline beating systems for the high-resource English-only sub-task of the SIGMORPHON 2021 Shared Task 1: a small ensemble that Dialpad's speech recognition team uses internally, a well-known off-the-shelf model, and a larger ensemble model comprising these and others. We additionally discuss the challenges related to the provided data, along with the processing steps we took.
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This paper describes the submission by the team from the Department of Computational Linguistics, Zurich University, to the Multilingual Grapheme-to-Phoneme Conversion (G2P) Task 1 of the SIGMORPHON 2021 challenge in the low and medium settings. The
submission is a variation of our 2020 G2P system, which serves as the baseline for this year's challenge. The system is a neural transducer that operates over explicit edit actions and is trained with imitation learning. For this challenge, we experimented with the following changes: a) emitting phoneme segments instead of single character phonemes, b) input character dropout, c) a mogrifier LSTM decoder (Melis et al., 2019), d) enriching the decoder input with the currently attended input character, e) parallel BiLSTM encoders, and f) an adaptive batch size scheduler. In the low setting, our best ensemble improved over the baseline, however, in the medium setting, the baseline was stronger on average, although for certain languages improvements could be observed.
In this paper we explore a very simple neural approach to mapping orthography to phonetic transcription in a low-resource context. The basic idea is to start from a baseline system and focus all efforts on data augmentation. We will see that some techniques work, but others do not.
This paper documents the UBC Linguistics team's approach to the SIGMORPHON 2021 Grapheme-to-Phoneme Shared Task, concentrating on the low-resource setting. Our systems expand the baseline model with simple modifications informed by syllable structure
and error analysis. In-depth investigation of test-set predictions shows that our best model rectifies a significant number of mistakes compared to the baseline prediction, besting all other submissions. Our results validate the view that careful error analysis in conjunction with linguistic knowledge can lead to more effective computational modeling.
Real-world applications often require improved models by leveraging *a range of cheap incidental supervision signals*. These could include partial labels, noisy labels, knowledge-based constraints, and cross-domain or cross-task annotations -- all ha
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The limits of applicability of vision-and language models are defined by the coverage of their training data. Tasks like vision question answering (VQA) often require commonsense and factual information beyond what can be learned from task-specific d
atasets. This paper investigates the injection of knowledge from general-purpose knowledge bases (KBs) into vision-and-language transformers. We use an auxiliary training objective that encourages the learned representations to align with graph embeddings of matching entities in a KB. We empirically study the relevance of various KBs to multiple tasks and benchmarks. The technique brings clear benefits to knowledge-demanding question answering tasks (OK-VQA, FVQA) by capturing semantic and relational knowledge absent from existing models. More surprisingly, the technique also benefits visual reasoning tasks (NLVR2, SNLI-VE). We perform probing experiments and show that the injection of additional knowledge regularizes the space of embeddings, which improves the representation of lexical and semantic similarities. The technique is model-agnostic and can expand the applicability of any vision-and-language transformer with minimal computational overhead.
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