Word embeddings

using pre-trained embeddings (Kim, 2014) [12]

使用预训练embedding

The optimal dimensionality of word embeddings is mostly task-dependent: a smaller dimensionality works better for more syntactic tasks such as named entity recognition (Melamud et al., 2016) [44] or part-of-speech (POS) tagging (Plank et al., 2016) [32], while a larger dimensionality is more useful for more semantic tasks such as sentiment analysis (Ruder et al., 2016) [45]

最优维度的word embedding和任务相关：对文法型任务（NER/命名实体识别、POS tagging/词性标注）低维更好，语义型任务（SA情感分析）高维更好。

Depth

use deep Bi-LSTMs, typically consisting of 3-4 layers, e.g. for POS tagging (Plank et al., 2016) and semantic role labelling (He et al., 2017) [33]. Models for some tasks can be even deeper, cf. Google's NMT model with 8 encoder and 8 decoder layers (Wu et al., 2016) [20]

用3-4层deep Bi-LSTM做POS tagging和SRL（语义角色标注）。Google的NMT模型用了8个encoder和decoder层。

performance improvements of making the model deeper than 2 layers are minimal (Reimers & Gurevych, 2017) [46]

多于两层的提升很微弱

For classification, deep or very deep models perform well only with character-level input

对于分类问题，仅在字级别的任务中深度模型效果更好

shallow word-level models are still the state-of-the-art (Zhang et al., 2015; Conneau et al., 2016; Le et al., 2017) [28, 29, 30]

浅层词级别模型仍然是最优解

Layer connections

vanishing gradient problem 梯度消失问题

Highway layers (Srivastava et al., 2015) [1] are inspired by the gates of an LSTM.

Highway层思想来自LSTM的门结构。

Highway layers have been used pre-dominantly to achieve state-of-the-art results for language modelling (Kim et al., 2016; Jozefowicz et al., 2016; Zilly et al., 2017) [2, 3, 4], but have also been used for other tasks such as speech recognition (Zhang et al., 2016) [5]

Highway层主要用于语言模型，但也用于语音识别。

Residual connections (He et al., 2016) [6] have been first proposed for computer vision and were the main factor for winning ImageNet 2016.

残差连接在研究视觉问题中提出，是ImageNet 2016比赛获胜的主要因素。

This simple modification mitigates the vanishing gradient problem, as the model can default to using the identity function if the layer is not beneficial.

这个简单改进显著减弱梯度消失问题，如果该层没用就直接将输入数值输出。

dense connections (Huang et al., 2017) [7] (best paper award at CVPR 2017) add direct connections from each layer to all subsequent layers.

dense连接将任何层都连接所有前面的层。

Dense connections have been successfully used in computer vision. They have also found to be useful for Multi-Task Learning of different NLP tasks (Ruder et al., 2017) [49], while a residual variant that uses summation has been shown to consistently outperform residual connections for neural machine translation (Britz et al., 2017) [27].

sense连接在视觉领域成功运用。在NLP多任务学习中也有用，NMT任务中残差求和方式比残差连接更好。

Dropout

While batch normalisation in computer vision has made other regularizers obsolete in most applications, dropout (Srivasta et al., 2014) [8] is still the go-to regularizer for deep neural networks in NLP.

大量视觉问题中batch正则替代所有其他正则方法。NLP问题中普遍对DNN做dropout。

A dropout rate of 0.5 has been shown to be effective in most scenarios (Kim, 2014).

dropout rate=0.5普遍认为较好。

The main problem hindering dropout in NLP has been that it could not be applied to recurrent connections, as the aggregating dropout masks would effectively zero out embeddings over time.

dropout的问题在于不能用于RNN，因为多次dropout会将embedding数据置零。

Recurrent dropout (Gal & Ghahramani, 2016) [11]...

Multi-task learning...

Attention...

Optimization

Adam (Kingma & Ba, 2015) [21] is one of the most popular and widely used optimization algorithms and often the go-to optimizer for NLP researchers. It is often thought that Adam clearly outperforms vanilla stochastic gradient descent (SGD).

Adam被普遍使用，尤其在NLP领域。一般认为Adam比vanilla SGD好。

while it converges much faster than SGD, it has been observed that SGD with learning rate annealing slightly outperforms Adam (Wu et al., 2016). Recent work furthermore shows that SGD with properly tuned momentum outperforms Adam (Zhang et al., 2017) [42].

Adam收敛比SGD更快，不过SGD使用逐渐减弱的学习率比Adam更好。

Ensembling

Ensembling is an important way to ensure that results are still reliable if the diversity of the evaluated models increases (Denkowski & Neubig, 2017).

Ensembling集成多个异质模型非常有用。

ensembling different checkpoints of a model has been shown to be effective (Jean et al., 2015; Sennrich et al., 2016) [51, 52]

集成多个版本的模型实际有效。

Hyperparameter optimization

simply tuning the hyperparameters of our model can yield significant improvements over baselines.

简单调整超参数就足够。

Automatic tuning of hyperparameters of an LSTM has led to state-of-the-art results in language modeling, outperforming models that are far more complex (Melis et al., 2017).

对LSTM自动调超参可获得最佳语言模型，比更复杂的模型更好。

LSTM tricks...

Task-specific best practices

Classification

CNNs have been popular for classification tasks in NLP.

CNN被广泛用于NLP的分类问题。

Combining filter sizes near the optimal filter size, e.g. (3,4,5) performs best (Kim, 2014; Kim et al., 2016).

在最佳尺度附近组合多个尺度的filter（比如3,4,5）实际更好。

The optimal number of feature maps is in the range of 50-600 (Zhang & Wallace, 2015) [59].

feature maps的最优值范围大致在50-600。

1-max-pooling outperforms average-pooling and $k$ -max pooling (Zhang & Wallace, 2015).

1-max-pooling被认为优于average-pooling和k-max pooling。

Sequence labelling

Tagging scheme BIO, which marks the first token in a segment with a B- tag, all remaining tokens in the span with an I-tag, and tokens outside of segments with an O- tag

BIO方式：segment内：开头标B，其他标I；外部标O

IOBES, which in addition distinguishes between single-token entities (S-) and the last token in a segment (E-).

IOBES方式：segment内：开头标B，中间标I，末尾标E；外部标O；单个token的segment标S。

Using IOBES and BIO yield similar performance (Lample et al., 2017)

IOBES和BIO效果接近。

CRF output layer If there are any dependencies between outputs, such as in named entity recognition the final softmax layer can be replaced with a linear-chain conditional random field (CRF). This has been shown to yield consistent improvements for tasks that require the modelling of constraints (Huang et al., 2015; Max & Hovy, 2016; Lample et al., 2016) [60, 61, 62].

如果输出节点之间有依赖，比如NER问题，终端softmax层可以被linear-chain CRF替换。实际证明在对约束建模的各类问题中都能有提升。

Constrained decoding...

Natural language generation

many of the tips presented so far stem from advances in language modelling

现有提升许多来自语言模型的进步。

Modelling coverage A checklist can be used if it is known in advances, which entities should be mentioned in the output, e.g. ingredients in recipes (Kiddon et al., 2016) [63]

尽量考虑先验知识，比如领域实体词。

...

Neural machine translation

While neural machine translation (NMT) is an instance of NLG, NMT receives so much attention that many best practices or hyperparameter choices apply exclusively to it.

NMT是NLG的特例，但是受到额外关注。

- Embedding dimensionality

2048-dimensional embeddings yield the best performance, but only do so by a small margin.

2048维是最佳，但是领先有限。

Even 128-dimensional embeddings perform surprisingly well and converge almost twice as quickly (Britz et al., 2017).

128维表现很好，收敛更快。

- Encoder and decoder depth...

The encoder does not need to be deeper than $2-4 layers.$

$encoder不需要超过2-4层。$

$Deeper models outperform shallower ones, but more than 4 is not necessary for the decoder$

$深度模型比浅层好，但是decoder超过4层没必要。$

$- Directionality$

$Bidirectional encoders outperform unidirectional ones by a small margin...$

双向encoder比单向更好，但是很微弱

$- Beam search strategy$

$Medium beam sizes around 10 with length normalization penalty of 1.0 (Wu et al., 2016) yield the best performance (Britz et al., 2017).$

平均长度10，结合正则惩罚系数1.0左右最佳。

$Sub-word translation...$

posted on 2019-03-22 18:22 冰山上的博客阅读(444) 评论(0) 编辑收藏举报

Deep Learning for NLP Best Practices - reading notes