[PDF] Top 20 Modeling Confidence in Sequence to Sequence Models

... Two baseline systems are presented in this ex- periment. To measure the difficulty of the task, we use the BEER evaluation metric as comparison, which has been performing competitively in the WMT Metric evaluations ... See full document

... In machine learning, the idea of incremental- ity has already played a role in several problem statements, such as inferring the tree structure of a sentence (Jacob et al., 2018), parsing (K¨ohn and Menzel, 2014), or in ... See full document

... Sequence-to-sequence models have shown strong performance across a broad range of ...that sequence- based AMR models are robust against ordering variations of graph-to-sequence ... See full document

... Sequence-to-sequence models have been applied to the conversation response gen- eration problem where the source se- quence is the conversation history and the target sequence is the ... See full document

... Paradigm completion. SIGMORPHON hosted two shared tasks on paradigm completion (Cotterell et al., 2016, 2017), in order to encour- age the development of systems for the task. One approach is to treat it as a string ... See full document

... Sequence-to-sequence models are a powerful workhorse of ...sparse sequence-to-sequence models, rooted in a new family of α-entmax transformations, which includes softmax and ... See full document

... In this work, we focus on morphological rein- flection (MRI), the task of mapping one inflected form of a lemma to another, given the morpholog- ical properties of the target, e.g., (smiling, Past- Part) → smiled. The ... See full document

... Prior works in this field leverage a language model for the target style, achieving transforma- tion either using phrase tables (Xu et al., 2012), or by inserting relevant adjectives and adverbs (Saha Roy et al., 2015). ... See full document

... our sequence-to-sequence ...our sequence-to-sequence model with soft attention and transition state features, while Hard+feats is using hard ...The sequence-to-sequence mod- els ... See full document

... Sequence-to-Sequence models built around the encoder-decoder paradigm (Sutskever et ...a sequence- to-sequence model in a general purpose deep learning framework such as TensorFlow ... See full document

... (Table 2) never occurring in training is a result of a high precision (92.65) coupled with a low re- call (4.45). This suggests some limited capacity to generalize to items not seen in training. A se- lectively chosen ... See full document

... We interpret the predictions of any black- box structured input-structured output model around a specific input-output pair. Our method returns an “explanation” con- sisting of groups of input-output tokens that are ... See full document

... Our character-based model with 1024 hidden units achieved 12.48 per-description perplexity, marginally better than 12.58 obtained with a word- based neural network model reported in that work. Thus, we see that ... See full document

... With some experiments on training Seq2Seq mod- els, we have discovered an interesting fact: By appending the control signals in the end of the input sequences, after seeing sufficient amount of such data, the Seq2Seq ... See full document

... From the language modeling experiments in Sec- tion 3.2, we hypothesized that an RNN layer be- comes more expressive, when the dense layer is replaced by a larger layer with predefined sparse- ness and the same ... See full document

... MMI and Adver-REGS outperform Seq2Seq base- lines in terms of BLUE, PPL and distinct mea- sures. That’s because both MMI and Adver- REGS further consider some reward functions in the optimization process to encourage ... See full document

... We can now compare attacks under the three con- straints Unconstrained, kNN and CharSwap and draw conclusions on their capacity to preserve meaning in the source and destroy it in the tar- get. Attacks are conducted on ... See full document

... to modeling the sentiment of the ...multimodal models have been proposed that are highly effective at learning multimodal representations for multi- modal sentiment analysis (Chen et ... See full document

... neural sequence models in grammatical error detection and cor- rection (Yuan and Briscoe, 2016; Rei and Yan- nakoudakis, 2016; Yannakoudakis et ...network sequence models for this prob- lem: ... See full document

... Context Window. The diacritization of Arabic words is highly sensitive to context. Character rep- resentations significantly increase the size of the source and target sequences. This leads to a well known limitation of ... See full document