prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. what previous RNN models do they compare with?
Context 1: Introduction
Long short term memory (LSTM) units BIBREF1 are popular for many sequence modeling tasks and are used extensively in language modeling  A key to their success is their articulated gating structure, which allows for more control over the information passed along the recurrence  However, despite the sophistication of the gating mechanisms employed in LSTMs and similar recurrent units, the input and context vectors are treated with simple linear transformations prior to gating  Non-linear transformations such as convolutions BIBREF2 have been used, but these have not achieved the performance of well regularized LSTMs for language modeling BIBREF3  
A natural way to improve the expressiveness of linear transformations is to increase the number of dimensions of the input and context vectors, but this comes with a significant increase in the number of parameters which may limit generalizability  An example is shown in Figure FIGREF1 , where LSTMs performance decreases with the increase in dimensions of the input and context vectors  Moreover, the semantics of the input and context vectors are different, suggesting that each may benefit from specialized treatment 
Guided by these insights, we introduce a new recurrent unit, the Pyramidal Recurrent Unit (PRU), which is based on the LSTM gating structure  Figure FIGREF2 provides an overview of the PRU  At the heart of the PRU is the pyramidal transformation (PT), which uses subsampling to effect multiple views of the input vector  The subsampled representations are combined in a pyramidal fusion structure, resulting in richer interactions between the individual dimensions of the input vector than is possible with a linear transformation  Context vectors, which have already undergone this transformation in the previous cell, are modified with a grouped linear transformation (GLT) which allows the network to learn latent representations in high dimensional space with fewer parameters and better generalizability (see Figure FIGREF1 ) 
We show that PRUs can better model contextual information and demonstrate performance gains on the task of language modeling  The PRU improves the perplexity of the current state-of-the-art language model BIBREF0 by up to 1 3 points, reaching perplexities of 56 56 and 64 53 on the Penn Treebank and WikiText2 datasets while learning 15-20% fewer parameters  Replacing an LSTM with a PRU results in improvements in perplexity across a variety of experimental settings  We provide detailed ablations which motivate the design of the PRU architecture, as well as detailed analysis of the effect of the PRU on other components of the language model 
Related work
Multiple methods, including a variety of gating structures and transformations, have been proposed to improve the performance of recurrent neural networks (RNNs)  We first describe these approaches and then provide an overview of recent work in language modeling 
Pyramidal Recurrent Units
We introduce Pyramidal Recurrent Units (PRUs), a new RNN architecture which improves modeling of context by allowing for higher dimensional vector representations while learning fewer parameters  Figure FIGREF2 provides an overview of PRU  We first elaborate on the details of the pyramidal transformation and the grouped linear transformation  We then describe our recurrent unit, PRU 
Pyramidal transformation for input
The basic transformation in many recurrent units is a linear transformation INLINEFORM0 defined as: DISPLAYFORM0
where INLINEFORM0 are learned weights that linearly map INLINEFORM1 to INLINEFORM2   To simplify notation, we omit the biases 
Motivated by successful applications of sub-sampling in computer vision (e g , BIBREF22 , BIBREF23 , BIBREF9 , BIBREF24 ), we subsample input vector INLINEFORM0 into INLINEFORM1 pyramidal levels to achieve representation of the input vector at multiple scales  This sub-sampling operation produces INLINEFORM2 vectors, represented as INLINEFORM3 , where INLINEFORM4 is the sampling rate and INLINEFORM5   We learn scale-specific transformations INLINEFORM6 for each INLINEFORM7   The transformed subsamples are concatenated to produce the pyramidal analog to INLINEFORM8 , here denoted as INLINEFORM9 : DISPLAYFORM0
where INLINEFORM0 indicates concatenation  We note that pyramidal transformation with INLINEFORM1 is the same as the linear transformation 
To improve gradient flow inside the recurrent unit, we combine the input and output using an element-wise sum (when dimension matches) to produce residual analog of pyramidal transformation, as shown in Figure FIGREF2 BIBREF25  
We sub-sample the input vector INLINEFORM0 into INLINEFORM1 pyramidal levels using the kernel-based approach BIBREF8 , BIBREF9   Let us assume that we have a kernel INLINEFORM2 with INLINEFORM3 elements  Then, the input vector INLINEFORM4 can be sub-sampled as: DISPLAYFORM0
where INLINEFORM0 represents the stride and INLINEFORM1  
The number of parameters learned by the linear transformation and the pyramidal transformation with INLINEFORM0 pyramidal levels to map INLINEFORM1 to INLINEFORM2 are INLINEFORM3 and INLINEFORM4 respectively  Thus, pyramidal transformation reduces the parameters of a linear transformation by a factor of INLINEFORM5   For example, the pyramidal transformation (with INLINEFORM6 and INLINEFORM7 ) learns INLINEFORM8 fewer parameters than the linear transformation 
Grouped linear transformation for context
Many RNN architectures apply linear transformations to both the input and context vector  However, this may not be ideal due to the differing semantics of each vector  In many NLP applications including language modeling, the input vector is a dense word embedding which is shared across all contexts for a given word in a dataset  In contrast, the context vector is highly contextualized by the current sequence  The differences between the input and context vector motivate their separate treatment in the PRU architecture 
The weights learned using the linear transformation (Eq  EQREF9 ) are reused over multiple time steps, which makes them prone to over-fitting BIBREF26   To combat over-fitting, various methods, such as variational dropout BIBREF26 and weight dropout BIBREF0 , have been proposed to regularize these recurrent connections  To further improve generalization abilities while simultaneously enabling the recurrent unit to learn representations at very high dimensional space, we propose to use grouped linear transformation (GLT) instead of standard linear transformation for recurrent connections BIBREF27   While pyramidal and linear transformations can be applied to transform context vectors, our experimental results in Section SECREF39 suggests that GLTs are more effective 
The linear transformation INLINEFORM0 maps INLINEFORM1 linearly to INLINEFORM2   Grouped linear transformations break the linear interactions by factoring the linear transformation into two steps  First, a GLT splits the input vector INLINEFORM3 into INLINEFORM4 smaller groups such that INLINEFORM5   Second, a linear transformation INLINEFORM6 is applied to map INLINEFORM7 linearly to INLINEFORM8 , for each INLINEFORM9   The INLINEFORM10 resultant output vectors INLINEFORM11 are concatenated to produce the final output vector INLINEFORM12   DISPLAYFORM0
GLTs learn representations at low dimensionality  Therefore, a GLT requires INLINEFORM0 fewer parameters than the linear transformation  We note that GLTs are subset of linear transformations  In a linear transformation, each neuron receives an input from each element in the input vector while in a GLT, each neuron receives an input from a subset of the input vector  Therefore, GLT is the same as a linear transformation when INLINEFORM1  
Pyramidal Recurrent Unit
We extend the basic gating architecture of LSTM with the pyramidal and grouped linear transformations outlined above to produce the Pyramidal Recurrent Unit (PRU), whose improved sequence modeling capacity is evidenced in Section SECREF4  
At time INLINEFORM0 , the PRU combines the input vector INLINEFORM1 and the previous context vector (or previous hidden state vector) INLINEFORM2 using the following transformation function as: DISPLAYFORM0
where INLINEFORM0 indexes the various gates in the LSTM model, and INLINEFORM1 and INLINEFORM2 represents the pyramidal and grouped linear transformations defined in Eqns  EQREF10 and EQREF15 , respectively 
We will now incorporate INLINEFORM0 into LSTM gating architecture to produce PRU  At time INLINEFORM1 , a PRU cell takes INLINEFORM2 , INLINEFORM3 , and INLINEFORM4 as inputs to produce forget INLINEFORM5 , input INLINEFORM6 , output INLINEFORM7 , and content INLINEFORM8 gate signals  The inputs are combined with these gate signals to produce context vector INLINEFORM9 and cell state INLINEFORM10   Mathematically, the PRU with the LSTM gating architecture can be defined as: DISPLAYFORM0
where INLINEFORM0 represents the element-wise multiplication operation, and INLINEFORM1 and INLINEFORM2 are the sigmoid and hyperbolic tangent activation functions  We note that LSTM is a special case of PRU when INLINEFORM3 = INLINEFORM4 =1 
Experiments
To showcase the effectiveness of the PRU, we evaluate the performance on two standard datasets for word-level language modeling and compare with state-of-the-art methods  Additionally, we provide a detailed examination of the PRU and its behavior on the language modeling tasks 
Set-up
Following recent works, we compare on two widely used datasets, the Penn Treebank (PTB) BIBREF28 as prepared by BIBREF29 and WikiText2 (WT-2) BIBREF20   For both datasets, we follow the same training, validation, and test splits as in BIBREF0  
We extend the language model, AWD-LSTM BIBREF0 , by replacing LSTM layers with PRU  Our model uses 3-layers of PRU with an embedding size of 400  The number of parameters learned by state-of-the-art methods vary from 18M to 66M with majority of the methods learning about 22M to 24M parameters on the PTB dataset  For a fair comparison with state-of-the-art methods, we fix the model size to 19M and vary the value of INLINEFORM0 and hidden layer sizes so that total number of learned parameters is similar across different configurations  We use 1000, 1200, and 1400 as hidden layer sizes for values of INLINEFORM1 =1,2, and 4, respectively  We use the same settings for the WT-2 dataset  We set the number of pyramidal levels INLINEFORM2 to two in our experiments and use average pooling for sub-sampling  These values are selected based on our ablation experiments on the validation set (Section SECREF39 )  We measure the performance of our models in terms of word-level perplexity  We follow the same training strategy as in BIBREF0  
To understand the effect of regularization methods on the performance of PRUs, we perform experiments under two different settings: (1) Standard dropout: We use a standard dropout BIBREF12 with probability of 0 5 after embedding layer, the output between LSTM layers, and the output of final LSTM layer  (2) Advanced dropout: We use the same dropout techniques with the same dropout values as in BIBREF0   We call this model as AWD-PRU 
Results
Table TABREF23 compares the performance of the PRU with state-of-the-art methods  We can see that the PRU achieves the best performance with fewer parameters 
PRUs achieve either the same or better performance than LSTMs  In particular, the performance of PRUs improves with the increasing value of INLINEFORM0   At INLINEFORM1 , PRUs outperform LSTMs by about 4 points on the PTB dataset and by about 3 points on the WT-2 dataset  This is explained in part by the regularization effect of the grouped linear transformation (Figure FIGREF1 )  With grouped linear and pyramidal transformations, PRUs learn rich representations at very high dimensional space while learning fewer parameters  On the other hand, LSTMs overfit to the training data at such high dimensions and learn INLINEFORM2 to INLINEFORM3 more parameters than PRUs 
With the advanced dropouts, the performance of PRUs improves by about 4 points on the PTB dataset and 7 points on the WT-2 dataset  This further improves with finetuning on the PTB (about 2 points) and WT-2 (about 1 point) datasets 
For similar number of parameters, the PRU with standard dropout outperforms most of the state-of-the-art methods by large margin on the PTB dataset (e g  RAN BIBREF7 by 16 points with 4M less parameters, QRNN BIBREF33 by 16 points with 1M more parameters, and NAS BIBREF31 by 1 58 points with 6M less parameters)  With advanced dropouts, the PRU delivers the best performance  On both datasets, the PRU improves the perplexity by about 1 point while learning 15-20% fewer parameters 
PRU is a drop-in replacement for LSTM, therefore, it can improve language models with modern inference techniques such as dynamic evaluation BIBREF21   When we evaluate PRU-based language models (only with standard dropout) with dynamic evaluation on the PTB test set, the perplexity of PRU ( INLINEFORM0 ) improves from 62 42 to 55 23 while the perplexity of an LSTM ( INLINEFORM1 ) with similar settings improves from 66 29 to 58 79; suggesting that modern inference techniques are equally applicable to PRU-based language models 
Analysis
It is shown above that the PRU can learn representations at higher dimensionality with more generalization power, resulting in performance gains for language modeling  A closer analysis of the impact of the PRU in a language modeling system reveals several factors that help explain how the PRU achieves these gains 
As exemplified in Table TABREF34 , the PRU tends toward more confident decisions, placing more of the probability mass on the top next-word prediction than the LSTM  To quantify this effect, we calculate the entropy of the next-token distribution for both the PRU and the LSTM using 3687 contexts from the PTB validation set  Figure FIGREF32 shows a histogram of the entropies of the distribution, where bins of size 0 23 are used to effect categories  We see that the PRU more often produces lower entropy distributions corresponding to higher confidences for next-token choices  This is evidenced by the mass of the red PRU curve lying in the lower entropy ranges compared to the blue LSTM's curve  The PRU can produce confident decisions in part because more information is encoded in the higher dimensional context vectors 
The PRU has the ability to model individual words at different resolutions through the pyramidal transform; which provides multiple paths for the gradient to the embedding layer (similar to multi-task learning) and improves the flow of information  When considering the embeddings by part of speech, we find that the pyramid level 1 embeddings exhibit higher variance than the LSTM across all POS categories (Figure FIGREF33 ), and that pyramid level 2 embeddings show extremely low variance  We hypothesize that the LSTM must encode both coarse group similarities and individual word differences into the same vector space, reducing the space between individual words of the same category  The PRU can rely on the subsampled embeddings to account for coarse-grained group similarities, allowing for finer individual word distinctions in the embedding layer  This hypothesis is strengthened by the entropy results described above: a model which can make finer distinctions between individual words can more confidently assign probability mass  A model that cannot make these distinctions, such as the LSTM, must spread its probability mass across a larger class of similar words 
Saliency analysis using gradients help identify relevant words in a test sequence that contribute to the prediction BIBREF34 , BIBREF35 , BIBREF36   These approaches compute the relevance as the squared norm of the gradients obtained through back-propagation  Table TABREF34 visualizes the heatmaps for different sequences  PRUs, in general, give more relevance to contextual words than LSTMs, such as southeast (sample 1), cost (sample 2), face (sample 4), and introduced (sample 5), which help in making more confident decisions  Furthermore, when gradients during back-propagation are visualized BIBREF37 (Table TABREF34 ), we find that PRUs have better gradient coverage than LSTMs, suggesting PRUs use more features than LSTMs that contributes to the decision  This also suggests that PRUs update more parameters at each iteration which results in faster training  Language model in BIBREF0 takes 500 and 750 epochs to converge with PRU and LSTM as a recurrent unit, respectively 
Ablation studies
In this section, we provide a systematic analysis of our design choices  Our training methodology is the same as described in Section SECREF19 with the standard dropouts  For a thorough understanding of our design choices, we use a language model with a single layer of PRU and fix the size of embedding and hidden layers to 600  The word-level perplexities are reported on the validation sets of the PTB and the WT-2 datasets 
The two hyper-parameters that control the trade-off between performance and number of parameters in PRUs are the number of pyramidal levels INLINEFORM0 and groups INLINEFORM1   Figure FIGREF35 provides a trade-off between perplexity and recurrent unit (RU) parameters 
Variable INLINEFORM0 and fixed INLINEFORM1 : When we increase the number of pyramidal levels INLINEFORM2 at a fixed value of INLINEFORM3 , the performance of the PRU drops by about 1 to 4 points while reducing the total number of recurrent unit parameters by up to 15%  We note that the PRU with INLINEFORM4 at INLINEFORM5 delivers similar performance as the LSTM while learning about 15% fewer recurrent unit parameters 
Fixed INLINEFORM0 and variable INLINEFORM1 : When we vary the value of INLINEFORM2 at fixed number of pyramidal levels INLINEFORM3 , the total number of recurrent unit parameters decreases significantly with a minimal impact on the perplexity  For example, PRUs with INLINEFORM4 and INLINEFORM5 learns 77% fewer recurrent unit parameters while its perplexity (lower is better) increases by about 12% in comparison to LSTMs  Moreover, the decrease in number of parameters at higher value of INLINEFORM6 enables PRUs to learn the representations in high dimensional space with better generalizability (Table TABREF23 ) 
Table TABREF43 shows the impact of different transformations of the input vector INLINEFORM0 and the context vector INLINEFORM1   We make following observations: (1) Using the pyramidal transformation for the input vectors improves the perplexity by about 1 point on both the PTB and WT-2 datasets while reducing the number of recurrent unit parameters by about 14% (see R1 and R4)  We note that the performance of the PRU drops by up to 1 point when residual connections are not used (R4 and R6)  (2) Using the grouped linear transformation for context vectors reduces the total number of recurrent unit parameters by about 75% while the performance drops by about 11% (see R3 and R4)  When we use the pyramidal transformation instead of the linear transformation, the performance drops by up to 2% while there is no significant drop in the number of parameters (R4 and R5) 
We set sub-sampling kernel INLINEFORM0 (Eq  EQREF12 ) with stride INLINEFORM1 and size of 3 ( INLINEFORM2 ) in four different ways: (1) Skip: We skip every other element in the input vector  (2) Convolution: We initialize the elements of INLINEFORM3 randomly from normal distribution and learn them during training the model  We limit the output values between -1 and 1 using INLINEFORM4 activation function to make training stable  (3) Avg  pool: We initialize the elements of INLINEFORM5 to INLINEFORM6   (4) Max pool: We select the maximum value in the kernel window INLINEFORM7  
Table TABREF45 compares the performance of the PRU with different sampling methods  Average pooling performs the best while skipping give comparable performance  Both of these methods enable the network to learn richer word representations while representing the input vector in different forms, thus delivering higher performance  Surprisingly, a convolution-based sub-sampling method does not perform as well as the averaging method  The INLINEFORM0 function used after convolution limits the range of output values which are further limited by the LSTM gating structure, thereby impeding in the flow of information inside the cell  Max pooling forces the network to learn representations from high magnitude elements, thus distinguishing features between elements vanishes, resulting in poor performance 
Conclusion
We introduce the Pyramidal Recurrent Unit, which better model contextual information by admitting higher dimensional representations with good generalizability  When applied to the task of language modeling, PRUs improve perplexity across several settings, including recent state-of-the-art systems  Our analysis shows that the PRU improves the flow of gradient and expand the word embedding subspace, resulting in more confident decisions  Here we have shown improvements for language modeling  In future, we plan to study the performance of PRUs on different tasks, including machine translation and question answering  In addition, we will study the performance of the PRU on language modeling with more recent inference techniques, such as dynamic evaluation and mixture of softmax 
Acknowledgments
This research was supported by NSF (IIS 1616112, III 1703166), Allen Distinguished Investigator Award, and gifts from Allen Institute for AI, Google, Amazon, and Bloomberg  We are grateful to Aaron Jaech, Hannah Rashkin, Mandar Joshi, Aniruddha Kembhavi, and anonymous reviewers for their helpful comments 
Context 2: Introduction
The use of RNNs in the field of Statistical Machine Translation (SMT) has revolutionised the approaches to automated translation  As opposed to traditional shallow SMT models, which require a lot of memory to run, these neural translation models require only a small fraction of memory used, about 5% BIBREF0   Also, neural translation models are optimized such that every module is trained to jointly improve translation quality  With that being said, one of the main downsides of neural translation models is the heavy corpus requirement in order to ensure learning of deeper contexts  This is where the application of these encoder decoder architectures in translation to and/or from morphologically rich languages takes a severe hit 
For any language pair, the efficiency of an MT system depends on two major factors: the availability and size of parallel corpus used for training and the syntactic divergence between the two languages i e morphological richness, word order differences, grammatical structure etc  BIBREF0   The main differences between the languages stem from the fact that languages similar to English are predominantly fusional languages whereas many of the morphologically rich languages are agglutinative in nature  The nature of morphologically rich languages being structurally and semantically discordant from languages like English adds to the difficulty of SMT involving such languages 
In morphologically rich languages, any suffix can be added to any verb or noun to simply mean one specific thing about that particular word that the suffix commonly represents (agglutination)  This means that there exists a lot of inflectional forms of the same noun and verb base words, conveying similar notions  For example, in Tamil, there are at least 30,000 inflectional forms of any given verb and about 5,000 forms of inflectional forms for any noun  The merged words carry information about part of speech (POS) tags, tense, plurality and so forth that are important for analyzing text for Machine Translation (MT)  Not only are these hidden meanings not captured, the corresponding root words are trained as different units, thereby increasing the complexity of developing such MT systems BIBREF1  
To add to the complexities of being a morphologically rich language, there are several factors unique to Tamil that make translation very difficult  The availability of parallel corpus for Tamil is very scarce  Most of the other models in the field of English–Tamil MT have made use of their own translation corpora that were manually created for the purposes of research  Most of these corpora are not available online for use 
Another issue specific to Tamil is the addition of suffix characters included to the words in the language for smoothness in pronunciation  These characters are of so many different types; there is a unique suffix for each and every consonant in the language  These suffixes degrade performance of MT because the same words with different such pronounciation-based suffixes will be taken as different words in training 
Also to take into consideration is the existence of two different forms of the language being used  Traditionally defined Tamil and its pronunciations aren't acoustically pleasing to use  There's no linguistic flow between syllables and its usage in verbal communication is time consuming  Therefore, there exists two forms of the language, the written form, rigid in structure and syntax, and the spoken form, in which the flow and pace of the language is given priority over syntax and correctness of spelling  This divide leads to the corpus having 2 different versions of the language that increase the vocabulary even with the same words  This can be evidently seen in the corpus between the sentences used in the Bible, which is in traditional Tamil and sentences from movie subtitles, being in spoken Tamil format 
To account for such difficulties, a trade-off between domain specificity and size of the corpus is integral in building an English–Tamil neural MT system 
Corpus
The corpus selected for this experiment was a combination of different corpora from various domains  The major part of the corpus was made up by the EnTam v2 corpus BIBREF2   This corpus contained sentences taken from parallel news articles, English and Tamil bible corpus and movie subtitles  It also comprised of a tourism corpus that was obtained from TDIL (Technology Development for Indian Languages) and a corpus created from Tamil novels and short stories from AU-KBC, Anna university  The complete corpus consisted of 197,792 sentences  Fig  FIGREF20 shows the skinny shift and heatmap representations of the relativity between the sentences in terms of their sentence lengths 
An extra monolingual Tamil corpus, collated from various online sources was used for the word2vec embedding of the Tamil target language to enhance the richness of context of the word vectors  It was also used to create the language model for the phrase-based SMT model  This corpus contained 567,772 sentences and was self-collected by combining hundreds of ancient Tamil scriptures, novels and poems by accessing the websites of popular online ebook libraries in Python using the urllib package  Since the sources had Tamil text in different encodings, the encoding scheme was standardized to be UTF-8 for the entirety of the monolingual and parallel corpora using the chardet package  The corpora were cleaned for any stray special characters, unnecessary html tags and website URLs 
Word2Vec
The word embeddings of the source and target language sentences are used as initial vectors of the model to improve contextualization  The skip gram model of the word2vec algorithm optimizes the vectors by accounting for the average log probability of context words given a source word  DISPLAYFORM0
where k is the context window taken for the vectorization, INLINEFORM0 refers to the INLINEFORM1 word of the corpus and INLINEFORM2 is the size of the training corpus in terms of the number of words  Here, the probabily INLINEFORM3 is computed as a hierarchical softmax of the product of the transpose of the output vector of INLINEFORM4 and the input vector of INLINEFORM5 for each and every pair over the entire vocabulary  The processes of negative sampling and subsampling of frequent words that were used in the original model aren't used in this experiment BIBREF3  
For the process of creating semantically meaningful word embeddings, a monolingual corpus of 569,772 Tamil sentences was used  This gave the vectors more contextual richness due to the increased size of the corpus as opposed to using just the bilingual corpus' target side sentences BIBREF3  
In the experiment, the word2vec model was trained using a vector size of 100 to ensure that the bulk of the limited memory of the GPU will be used for the neural attention translation model  It has been shown that any size over that of 150 used for word vectorization gives similar results and that a size of 100 performs close to the model with 150-sized word vectors BIBREF7   A standard size of 5 was used as window size and the model was trained over 7 worker threads simultaneously  A batch size of 50 words was used for training  The negative sampling was set at 1 as it is the nature of morphologically rich languages to have a lot of important words that don't occur more than once in the corpus  The gensim word2vec toolkit was used to implement this word embedding process BIBREF8  
Neural Translation Model
The model used for translation is the one implemented by Bahdanau et al  Bahdanau2014  A bidirectional LSTM encoder first takes the source sentence and encodes it into a context vector which acts as input for the decoder  The decoder is attention-based where the hidden states of the decoder get as input the weighted sum of all the hidden layer outputs of the encoder alongwith the output of the previous hidden layer and the previously decoded word  This provides a contextual reference into the source language sentence BIBREF4  
Neural Machine Translation models directly compute the probability of the target language sentence given the source language sentence, word by word for every time step  The model with a basic decoder without the attention module computes the log probability of target sentence given source sentence as the sum of log probabilities of every word given every word before that  The attention-based model, on the other hand, calculates: DISPLAYFORM0
where INLINEFORM0 is the number of words in the target sentence, INLINEFORM1 is the target sentence, INLINEFORM2 is the source sentence, INLINEFORM3 is the fixed length output vector of the encoder and INLINEFORM4 is the weighted sum of all the hidden layer outputs of the encoder at every time step  Both the encoder's output context vector and the weighted sum (known as attention vector) help to improve the quality of translation by enabling selective source sentence lookup 
The decoder LSTM computes: DISPLAYFORM0
where the probability is computed as a function of the decoder's output in the previous time step INLINEFORM0 , the hidden layer vector of the decoder in the current timestep INLINEFORM1 and the context vector from the attention mechanism INLINEFORM2   The context vector INLINEFORM3 for time step INLINEFORM4 is computed as a weighted sum of the output of the entire sentence using a weight parameter INLINEFORM5 : DISPLAYFORM0
where INLINEFORM0 is the number of tokens in the source sentence, INLINEFORM1 refers to the value of the hidden layer of the encoder at time step INLINEFORM2 , and INLINEFORM3 is the alignment parameter  This parameter is calculated by means of a feed forward neural network to ensure that the alignment model is free from the difficulties of contextualization of long sentences into a single vector  The feed forward network is trained along with the neural translation model to jointly improve the performance of the translation  Mathematically, DISPLAYFORM0 DISPLAYFORM1
where INLINEFORM0 is the softmax output of the result of the feedforward network, INLINEFORM1 is the hidden state value of the decoder at timestep INLINEFORM2 and INLINEFORM3 is the encoder's hidden layer annotation at timestep INLINEFORM4   A concatenation of the forward and the reverse hidden layer parameters of the encoder is used at each step to compute the weights INLINEFORM5 for the attention mechanism  This is done to enable an overall context of the sentence, as opposed to a context of only all the previous words of the sentence for every word in consideration  Fig  FIGREF12 is the general architecture of the neural translation model without the Bidirectional LSTM encoder 
A global attention mechanism is preferred over local attention because the differences in the structures of the languages cannot be mapped efficiently to enable lookup into the right parts of the source sentence  Using local attention mechanism with a monotonic context lookup, where the region around INLINEFORM0 source word is looked up for the prediction of the INLINEFORM1 target word, is impractical because of the structural discordance between the English and Tamil sentences (see Figs  FIGREF37 and FIGREF44 )  The use of gaussian and other such distributions to facilitate local attention would also be inefficient because the existence of various forms of translations for the same source sentence involving morphological and structural variations that don't stay uniform through the entire corpus BIBREF5  
The No Peepholes (NP) variant of the LSTM cell, formulated in Greff et al  greff2015lstm is used in this experiment as it proved to give the best results amongst all the variants of an LSTM cell  It is specified by means of a gated mechanism designed to ensure that the vanishing gradient problem is prevented  LSTM maintains its hidden layer in two components, the cell vector INLINEFORM0 and the actual hidden layer output vector INLINEFORM1   The cell vector is ensured to never reach zero by means of a weighted sum of the previous layer's cell vector INLINEFORM2 regulated by the forget gate INLINEFORM3 and an activation of the weighted sum of the input INLINEFORM4 in the current timestep INLINEFORM5 and the previous timestep's hidden layer output vector INLINEFORM6   The combination is similarly regulated by the input gate INLINEFORM7   The hidden layer output is determined as an activation of the cell gate, regulated by the output gate INLINEFORM8   The interplay between these two vectors ( INLINEFORM9 and INLINEFORM10 ) at every timestep ensures that the problem of vanishing gradients doesn't occur  The three gates are also formed as a sigmoid of the weighted sum of the previous hidden layer output INLINEFORM11 and the input in the current timestep INLINEFORM12   The output generated out of the LSTM's hidden layer is specified as a weighted softmax over the hidden layer output INLINEFORM13   The learnable parameters of an LSTM cell are all the weights INLINEFORM14 and the biases INLINEFORM15   DISPLAYFORM0
The LSTM specified by equations 7 through 11 is the one used for the decoder of the model  The encoder uses a bidirectional RNN LSTM cell in which there are two hidden layer components INLINEFORM0 and INLINEFORM1 that contribute to the output INLINEFORM2 of each time step INLINEFORM3   Both the components have their own sets of LSTM equations in such a way that INLINEFORM4 for every timestep is computed from the first timestep till the INLINEFORM5 token is reached and INLINEFORM6 is computed from the INLINEFORM7 timestep backwards until the first token is reached  All the five vectors of the two components are all exactly the same as the LSTM equations specified with one variation in the computation of the result  DISPLAYFORM0
Morphological Segmentation
The morphological segmentation used is a semi-supervised extension to the generative probabilistic model of maximizing the probability of a INLINEFORM0 prefix,root,postfix INLINEFORM1 recursive split up of words based on an exhaustive combination of all possible morphemes  The details of this model are specified and extensively studied in Kohonen et al  kohonen2010semi  The model parameters INLINEFORM2 include the morph type count, morph token count of training data, the morph strings and their counts  The model is trained by maximizing the Maximum A Posteriori (MAP) probability using Bayes' rule: DISPLAYFORM0
where INLINEFORM0 refers to every word in the training lexicon  The prior INLINEFORM1 is estimated using the Minimum Description Length(MDL) principle  The likelihood INLINEFORM2 is estimated as: DISPLAYFORM0
where INLINEFORM0 refers to the intermediate analyses and INLINEFORM1 refers to the INLINEFORM2 morpheme of word INLINEFORM3  
An extension to the Viterbi algorithm is used for the decoding step based on exhaustive mapping of morphemes  To account for over-segmentation and under-segmentation issues associated with unsupervised morphological segmentation, extra parameters ( INLINEFORM0 ) and ( INLINEFORM1 ) are used with the cost function INLINEFORM2 DISPLAYFORM0
where INLINEFORM0 is the likelihood of the cost function, INLINEFORM1 describes the likelihood of contribution of the annotated dataset to the cost function and INLINEFORM2 is the likelihood of the labeled data  A decrease in the value of INLINEFORM3 will cause smaller segments and vice versa  INLINEFORM4 takes care of size discrepancies due to reduced availability of annotated corpus as compared to the training corpus BIBREF2 , BIBREF6  
The Python extension to the morphological segmentation tool morfessor 2 0 was used for this experiment to perform the segmentation  The annotation data for Tamil language collated and released by Anoop Kunchukkutan in the Indic NLP Library was used as the semi-supervised input to the model BIBREF9 , BIBREF6  
Experiment
The complexities of neural machine translation of morphologically rich languages were studied with respect to English to Tamil machine translation using the RNN LSTM Bi-directional encoder attention decoder architecture  To compare with a baseline system, a phrase based SMT system was implemented using the same corpus  The Factored SMT model with source-side preprocessing by Kumar et al  kumar2014improving was used as a reference for the translation between these language pairs  Also, an additional 569,772 monolingual Tamil sentences were used for the language model of the SMT system  The model used could be split up into various modules as expanded in Fig  FIGREF17  
Bucketing
The input source and target language sentences used for training were taken and divided into bucketed pairs of sentences of a fixed number of sizes  This relationship was determined by examining the distribution of words in the corpus primarily to minimize the number of PAD tokens in the sentence  The heat map of the number of words in the English–Tamil sentence pairs of the corpus revealed that the distribution is centered around the 10–20 words region  Therefore, more buckets in that region were applied as there would be enough number of examples in each of these bucket pairs for the model to learn about the sentences in each and every bucket  The exact scheme used for the RNNSearch models is specified by Fig  FIGREF21   The bucketing scheme for the RNNMorph model, involving morphs instead of words, was a simple shifted scheme of the one used in Fig  FIGREF21 , where every target sentence bucket count was increased uniformly by 5 
Model Details
Due to various computational constraints and lack of availability of comprehensive corpora, the vocabularies for English and Tamil languages for the RNNSearch model were restricted to 60,000 out of 67,768 and 150,000 out of 340,325 respectively  The vocabulary of the languages for the RNNMorph didn't have to be restricted and the actual number of words in the corpus i
Context 3: Introduction
Language model pretraining has advanced the state of the art in many NLP tasks ranging from sentiment analysis, to question answering, natural language inference, named entity recognition, and textual similarity  State-of-the-art pretrained models include ELMo BIBREF1, GPT BIBREF2, and more recently Bidirectional Encoder Representations from Transformers (Bert; BIBREF0)  Bert combines both word and sentence representations in a single very large Transformer BIBREF3; it is pretrained on vast amounts of text, with an unsupervised objective of masked language modeling and next-sentence prediction and can be fine-tuned with various task-specific objectives 
In most cases, pretrained language models have been employed as encoders for sentence- and paragraph-level natural language understanding problems BIBREF0 involving various classification tasks (e g , predicting whether any two sentences are in an entailment relationship; or determining the completion of a sentence among four alternative sentences)  In this paper, we examine the influence of language model pretraining on text summarization  Different from previous tasks, summarization requires wide-coverage natural language understanding going beyond the meaning of individual words and sentences  The aim is to condense a document into a shorter version while preserving most of its meaning  Furthermore, under abstractive modeling formulations, the task requires language generation capabilities in order to create summaries containing novel words and phrases not featured in the source text, while extractive summarization is often defined as a binary classification task with labels indicating whether a text span (typically a sentence) should be included in the summary 
We explore the potential of Bert for text summarization under a general framework encompassing both extractive and abstractive modeling paradigms  We propose a novel document-level encoder based on Bert which is able to encode a document and obtain representations for its sentences  Our extractive model is built on top of this encoder by stacking several inter-sentence Transformer layers to capture document-level features for extracting sentences  Our abstractive model adopts an encoder-decoder architecture, combining the same pretrained Bert encoder with a randomly-initialized Transformer decoder BIBREF3  We design a new training schedule which separates the optimizers of the encoder and the decoder in order to accommodate the fact that the former is pretrained while the latter must be trained from scratch  Finally, motivated by previous work showing that the combination of extractive and abstractive objectives can help generate better summaries BIBREF4, we present a two-stage approach where the encoder is fine-tuned twice, first with an extractive objective and subsequently on the abstractive summarization task 
We evaluate the proposed approach on three single-document news summarization datasets representative of different writing conventions (e g , important information is concentrated at the beginning of the document or distributed more evenly throughout) and summary styles (e g , verbose vs  more telegraphic; extractive vs  abstractive)  Across datasets, we experimentally show that the proposed models achieve state-of-the-art results under both extractive and abstractive settings  Our contributions in this work are three-fold: a) we highlight the importance of document encoding for the summarization task; a variety of recently proposed techniques aim to enhance summarization performance via copying mechanisms BIBREF5, BIBREF6, BIBREF7, reinforcement learning BIBREF8, BIBREF9, BIBREF10, and multiple communicating encoders BIBREF11  We achieve better results with a minimum-requirement model without using any of these mechanisms; b) we showcase ways to effectively employ pretrained language models in summarization under both extractive and abstractive settings; we would expect any improvements in model pretraining to translate in better summarization in the future; and c) the proposed models can be used as a stepping stone to further improve summarization performance as well as baselines against which new proposals are tested 
Background ::: Pretrained Language Models
Pretrained language models BIBREF1, BIBREF2, BIBREF0, BIBREF12, BIBREF13 have recently emerged as a key technology for achieving impressive gains in a wide variety of natural language tasks  These models extend the idea of word embeddings by learning contextual representations from large-scale corpora using a language modeling objective  Bidirectional Encoder Representations from Transformers (Bert; BIBREF0) is a new language representation model which is trained with a masked language modeling and a “next sentence prediction” task on a corpus of 3,300M words 
The general architecture of Bert is shown in the left part of Figure FIGREF2  Input text is first preprocessed by inserting two special tokens  [cls] is appended to the beginning of the text; the output representation of this token is used to aggregate information from the whole sequence (e g , for classification tasks)  And token [sep] is inserted after each sentence as an indicator of sentence boundaries  The modified text is then represented as a sequence of tokens $X=[w_1,w_2,\cdots ,w_n]$  Each token $w_i$ is assigned three kinds of embeddings: token embeddings indicate the meaning of each token, segmentation embeddings are used to discriminate between two sentences (e g , during a sentence-pair classification task) and position embeddings indicate the position of each token within the text sequence  These three embeddings are summed to a single input vector $x_i$ and fed to a bidirectional Transformer with multiple layers:
where $h^0=x$ are the input vectors; $\mathrm {LN}$ is the layer normalization operation BIBREF14; $\mathrm {MHAtt}$ is the multi-head attention operation BIBREF3; superscript $l$ indicates the depth of the stacked layer  On the top layer, Bert will generate an output vector $t_i$ for each token with rich contextual information 
Pretrained language models are usually used to enhance performance in language understanding tasks  Very recently, there have been attempts to apply pretrained models to various generation problems BIBREF15, BIBREF16  When fine-tuning for a specific task, unlike ELMo whose parameters are usually fixed, parameters in Bert are jointly fine-tuned with additional task-specific parameters 
Background ::: Extractive Summarization
Extractive summarization systems create a summary by identifying (and subsequently concatenating) the most important sentences in a document  Neural models consider extractive summarization as a sentence classification problem: a neural encoder creates sentence representations and a classifier predicts which sentences should be selected as summaries  SummaRuNNer BIBREF7 is one of the earliest neural approaches adopting an encoder based on Recurrent Neural Networks  Refresh BIBREF8 is a reinforcement learning-based system trained by globally optimizing the ROUGE metric  More recent work achieves higher performance with more sophisticated model structures  Latent BIBREF17 frames extractive summarization as a latent variable inference problem; instead of maximizing the likelihood of “gold” standard labels, their latent model directly maximizes the likelihood of human summaries given selected sentences  Sumo BIBREF18 capitalizes on the notion of structured attention to induce a multi-root dependency tree representation of the document while predicting the output summary  NeuSum BIBREF19 scores and selects sentences jointly and represents the state of the art in extractive summarization 
Background ::: Abstractive Summarization
Neural approaches to abstractive summarization conceptualize the task as a sequence-to-sequence problem, where an encoder maps a sequence of tokens in the source document $\mathbf {x} = [x_1,    , x_n]$ to a sequence of continuous representations $\mathbf {z} = [z_1,    , z_n]$, and a decoder then generates the target summary $\mathbf {y} = [y_1,    , y_m]$ token-by-token, in an auto-regressive manner, hence modeling the conditional probability: $p(y_1,    , y_m|x_1,    , x_n)$ 
BIBREF20 and BIBREF21 were among the first to apply the neural encoder-decoder architecture to text summarization  BIBREF6 enhance this model with a pointer-generator network (PTgen) which allows it to copy words from the source text, and a coverage mechanism (Cov) which keeps track of words that have been summarized  BIBREF11 propose an abstractive system where multiple agents (encoders) represent the document together with a hierarchical attention mechanism (over the agents) for decoding  Their Deep Communicating Agents (DCA) model is trained end-to-end with reinforcement learning  BIBREF9 also present a deep reinforced model (DRM) for abstractive summarization which handles the coverage problem with an intra-attention mechanism where the decoder attends over previously generated words  BIBREF4 follow a bottom-up approach (BottomUp); a content selector first determines which phrases in the source document should be part of the summary, and a copy mechanism is applied only to preselected phrases during decoding  BIBREF22 propose an abstractive model which is particularly suited to extreme summarization (i e , single sentence summaries), based on convolutional neural networks and additionally conditioned on topic distributions (TConvS2S) 
Fine-tuning Bert for Summarization ::: Summarization Encoder
Although Bert has been used to fine-tune various NLP tasks, its application to summarization is not as straightforward  Since Bert is trained as a masked-language model, the output vectors are grounded to tokens instead of sentences, while in extractive summarization, most models manipulate sentence-level representations  Although segmentation embeddings represent different sentences in Bert, they only apply to sentence-pair inputs, while in summarization we must encode and manipulate multi-sentential inputs  Figure FIGREF2 illustrates our proposed Bert architecture for Summarization (which we call BertSum) 
In order to represent individual sentences, we insert external [cls] tokens at the start of each sentence, and each [cls] symbol collects features for the sentence preceding it  We also use interval segment embeddings to distinguish multiple sentences within a document  For $sent_i$ we assign segment embedding $E_A$ or $E_B$ depending on whether $i$ is odd or even  For example, for document $[sent_1, sent_2, sent_3, sent_4, sent_5]$, we would assign embeddings $[E_A, E_B, E_A,E_B, E_A]$  This way, document representations are learned hierarchically where lower Transformer layers represent adjacent sentences, while higher layers, in combination with self-attention, represent multi-sentence discourse 
Position embeddings in the original Bert model have a maximum length of 512; we overcome this limitation by adding more position embeddings that are initialized randomly and fine-tuned with other parameters in the encoder 
Fine-tuning Bert for Summarization ::: Extractive Summarization
Let $d$ denote a document containing sentences $[sent_1, sent_2, \cdots , sent_m]$, where $sent_i$ is the $i$-th sentence in the document  Extractive summarization can be defined as the task of assigning a label $y_i \in \lbrace 0, 1\rbrace $ to each $sent_i$, indicating whether the sentence should be included in the summary  It is assumed that summary sentences represent the most important content of the document 
With BertSum, vector $t_i$ which is the vector of the $i$-th [cls] symbol from the top layer can be used as the representation for $sent_i$  Several inter-sentence Transformer layers are then stacked on top of Bert outputs, to capture document-level features for extracting summaries:
where $h^0=\mathrm {PosEmb}(T)$; $T$ denotes the sentence vectors output by BertSum, and function $\mathrm {PosEmb}$ adds sinusoid positional embeddings BIBREF3 to $T$, indicating the position of each sentence 
The final output layer is a sigmoid classifier:
where $h^L_i$ is the vector for $sent_i$ from the top layer (the $L$-th layer ) of the Transformer  In experiments, we implemented Transformers with $L=1, 2, 3$ and found that a Transformer with $L=2$ performed best  We name this model BertSumExt 
The loss of the model is the binary classification entropy of prediction $\hat{y}_i$ against gold label $y_i$  Inter-sentence Transformer layers are jointly fine-tuned with BertSum  We use the Adam optimizer with $\beta _1=0 9$, and $\beta _2=0 999$)  Our learning rate schedule follows BIBREF3 with warming-up ($ \operatorname{\operatorname{warmup}}=10,000$):
Fine-tuning Bert for Summarization ::: Abstractive Summarization
We use a standard encoder-decoder framework for abstractive summarization BIBREF6  The encoder is the pretrained BertSum and the decoder is a 6-layered Transformer initialized randomly  It is conceivable that there is a mismatch between the encoder and the decoder, since the former is pretrained while the latter must be trained from scratch  This can make fine-tuning unstable; for example, the encoder might overfit the data while the decoder underfits, or vice versa  To circumvent this, we design a new fine-tuning schedule which separates the optimizers of the encoder and the decoder 
We use two Adam optimizers with $\beta _1=0 9$ and $\beta _2=0 999$ for the encoder and the decoder, respectively, each with different warmup-steps and learning rates:
where $\tilde{lr}_{\mathcal {E}}=2e^{-3}$, and $\operatorname{\operatorname{warmup}}_{\mathcal {E}}=20,000$ for the encoder and $\tilde{lr}_{\mathcal {D}}=0 1$, and $\operatorname{\operatorname{warmup}}_{\mathcal {D}}=10,000$ for the decoder  This is based on the assumption that the pretrained encoder should be fine-tuned with a smaller learning rate and smoother decay (so that the encoder can be trained with more accurate gradients when the decoder is becoming stable) 
In addition, we propose a two-stage fine-tuning approach, where we first fine-tune the encoder on the extractive summarization task (Section SECREF8) and then fine-tune it on the abstractive summarization task (Section SECREF13)  Previous work BIBREF4, BIBREF23 suggests that using extractive objectives can boost the performance of abstractive summarization  Also notice that this two-stage approach is conceptually very simple, the model can take advantage of information shared between these two tasks, without fundamentally changing its architecture  We name the default abstractive model BertSumAbs and the two-stage fine-tuned model BertSumExtAbs 
Experimental Setup
In this section, we describe the summarization datasets used in our experiments and discuss various implementation details 
Experimental Setup ::: Summarization Datasets
We evaluated our model on three benchmark datasets, namely the CNN/DailyMail news highlights dataset BIBREF24, the New York Times Annotated Corpus (NYT; BIBREF25), and XSum BIBREF22  These datasets represent different summary styles ranging from highlights to very brief one sentence summaries  The summaries also vary with respect to the type of rewriting operations they exemplify (e g , some showcase more cut and paste operations while others are genuinely abstractive)  Table TABREF12 presents statistics on these datasets (test set); example (gold-standard) summaries are provided in the supplementary material

ground_truth: Variational LSTM, CharCNN, Pointer Sentinel-LSTM, RHN, NAS Cell, SRU, QRNN, RAN, 4-layer skip-connection LSTM, AWD-LSTM, Quantized LSTM

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. How were the human judgements assembled?
Context 2: e  67,768 words for English and 41,906 words for Tamil could be accommodated into the training  Words not in the vocabulary from the test set input and output were replaced with the universal INLINEFORM0 UNK INLINEFORM1 token, symbolizing an unknown word  The LSTM hidden layer size, the training batch size, and the vocabulary sizes of the languages, together, acted as a bottleneck  The model was run on a 2GB NVIDIA GeForce GT 650M card with 384 cores and the memory allotment was constrained to the limits of the GPU  Therefore, after repeated experimentation, it was determined that with a batch size of 16, the maximum hidden layer size possible was 500, which was the size used  Attempts to reduce the batch size resulted in poor convergence, and so the parameters were set to center around the batch size of 16  The models used were of 4 layers of LSTM hidden units in the bidirectional encoder and attention decoder 
The model used a Stochastic Gradient Descent (SGD) optimization algorithm with a sampled softmax loss of 512 per sample to handle large vocabulary size of the target language BIBREF10   The model was trained with a learning rate 1 0 and a decay of rate 0 5 enforced manually  Gradient clipping based on the global norm of 5 0 was carried out to prevent gradients exploding and going to unrecoverable values tending towards infinity  The model described is the one used in the Tensorflow BIBREF11 seq2seq library 
Results and Discussion
The BLEU metric parameters (modified 1-gram, 2-gram, 3-gram and 4-gram precision values) and human evaluation metrics of adequacy, fluency and relative ranking values were used to evaluate the performance of the models 
BLEU Evaluation
The BLEU scores obtained using the various models used in the experiment are tabulated in Table TABREF25  
The BLEU metric computes the BLEU unigram, bigram, trigram and BLEU-4 modified precision values, each micro-averaged over the test set sentences BIBREF7   It was observed, as expected, that the performance of the phrase-based SMT model was inferior to that of the RNNSearch model  The baseline RNNSearch system was further refined by using word2vec vectors to embed semantic understanding, as observed with the slight increase in the BLEU scores  Fig  FIGREF26 plots the BLEU scores as a line graph for visualization of the improvement in performance  Also, the 4-gram BLEU scores for the various models were plotted as a bar graph in Fig  FIGREF26
Due to the agglutinative and morphologically rich nature of the target language i e  Tamil, the use of morphological segmentation to split the words into morphemes further improved the BLEU precision values in the RNNMorph model  One of the reasons for the large extent of increase in the BLEU score could be attributed to the overall increase in the number of word units per sentence  Since the BLEU score computes micro-average precision scores, an increase in both the numerator and denominator of the precision scores is apparent with an increase in the number of tokens due to morphological segmentation of the target language  Thus, the numeric extent of the increase of accuracy might not efficiently describe the improvement in performance of the translation 
Human Evaluation
To ensure that the increase in BLEU score correlated to actual increase in performance of translation, human evaluation metrics like adequacy, precision and ranking values (between RNNSearch and RNNMorph outputs) were estimated in Table TABREF30   A group of 50 native people who were well-versed in both English and Tamil languages acted as annotators for the evaluation  A collection of samples of about 100 sentences were taken from the test set results for comparison  This set included a randomized selection of the translation results to ensure the objectivity of evaluation  Fluency and adequacy results for the RNNMorph results are tabulated  Adequacy rating was calculated on a 5-point scale of how much of the meaning is conveyed by the translation (All, Most, Much, Little, None)  The fluency rating was calculated based on grammatical correctness on a 5-point scale of (Flawless, Good, Non-native, Disfluent, Incomprehensive)  For the comparison process, the RNNMorph and the RNNSearch + Word2Vec models’ sentence level translations were individually ranked between each other, permitting the two translations to have ties in the ranking  The intra-annotator values were computed for these metrics and the scores are shown in Table TABREF32 BIBREF12 , BIBREF13  
The human evaluation Kappa co-efficient results are calculated with respect to: DISPLAYFORM0
It was observed that the ranking Kappa co-efficient for intra-annotator ranking of the RNNMorph model was at 0 573, higher that the 0 410 of the RNNSearch+Word2Vec model, implying that the annotators found the RNNMorph model to produce better results when compared to the RNNSearch + Word2Vec model 
Model Parameters
The learning rate decay through the training process of the RNNMorph model is showcased in the graph in Fig  FIGREF34   This process was done manually where the learning rate was decayed after the end of specific epochs based on an observed stagnation in perplexity The RNNMorph model achieved saturation of perplexities much earlier through the epochs than the RNNSearch + Word2Vec model  This conforms to the expected outcome as the morphological segmentation has reduced the vocabulary size of the target language from 340,325 words to a mere 41,906 morphs 
The error function used was the sampled SoftMax loss to ensure a large target vocabulary could be accommodated BIBREF10   A zoomed inset graph (Fig  FIGREF35 ) has been used to visualize the values of the error function for the RNNSearch + Word2Vec and RNNMorph models with 4 hidden layers  It can be seen that the RNNMorph model is consistently better in terms of the perplexity values through the time steps 
Attention Vectors
In order to further demonstrate the quality of the RNNMorph model, the attention vectors of both the RNNSearch with Word2Vec embedding and RNNMorph models are compared for several good translations in Figs  FIGREF37 and FIGREF44   It is observed that the reduction in vocabulary size has improved the source sentence lookup by quite an extent  Each cell in the heatmap displays the magnitude of the attention layer weight INLINEFORM0 for the INLINEFORM1 Tamil word and the INLINEFORM2 English word in the respective sentences  The intensity of black corresponds to the magnitude of the cell INLINEFORM3   Also, the attention vectors of the RNNSearch model with Word2Vec embeddings tend to attend to INLINEFORM4 EOS INLINEFORM5 token in the middle of the sentence leading to incomplete translations  This could be due to the fact that only 44% of the Tamil vocabulary and 74% of the English vocabulary is taken for training in this model, as opposed to 100% of English and Tamil words in the RNNMorph model 
Target vocabulary size
A very large target vocabulary is an inadvertent consequence of the morphological richness of the Tamil language  This creates a potential restriction on the accuracy of the model as many inflectional forms of the same word are trained as independent units  One of the advantages of morphological segmentation of Tamil text is that the target vocabulary size decreased from 340,325 to a mere 41,906  This reduction helps improve the performance of the translation as the occurrence of unknown tokens was reduced compared to the RNNSearch model  This morphologically segmented vocabulary is divided into a collection of morphological roots and inflections as individual units 
Repetitions
Some of the translations of the RNNMorph model have repetitions of the same phrases (Fig  FIGREF53 ), whereas such repetitions occur much less frequently in the RNNSearch predictions  Such translations would make for good results if the repetitions weren't present and all parts of the sentence occur just once  These repetitions might be due to the increase in the general sequence length of the target sentences because of the morphological segmentation  While it is true the target vocabulary size has decreased due to morphological segmentation, the RNNMorph has more input units (morphs) per sentence, which makes it more demanding of the LSTM's memory units and the feed forward network of the attention model  Additionally, this behavior could also be attributed to the errors in the semi-supervised morphological segmentation due to the complexities of the Tamil language and the extent of the corpus 
Model Outputs
The translation outputs of the RNNSearch + Word2Vec and Morph2Vec models for the same input sentences from the test set demonstrate the effectiveness of using a morphological segmentation tool and how the morphemes have changed the sentence to be more grammatically sound  It is also observed (from Fig  FIGREF55 ) that most of the translation sentences of the Morph2Vec model have no INLINEFORM0 UNK INLINEFORM1 tokens  They exist in the predictions mostly only due to a word in the English test sentence not present in the source vocabulary 
Related Work
Professors CN Krishnan, Sobha et al developed a machine-aided-translation (MAT) system similar to the Anusaakara English Hindi MT system, using a small corpus and very few transfer rules, available at AU-KBC website BIBREF14   Balajapally et al  balajapally2006multilingual developed an example based machine translation (EBMT) system with 700000 sentences for English to INLINEFORM0 Tamil, Kannada, Hindi INLINEFORM1 transliterated text BIBREF15 , BIBREF16   Renganathan renganathan2002interactive developed a rule based MT system for English and Tamil using grammar rules for the language pair  Vetrivel et al  vetrivel2010english used HMMs to align and translate English and Tamil parallel sentences to build an SMT system  Irvine et al  irvine2013combining tried to combine parallel and similar corpora to improve the performance of English to Tamil SMT amongst other languages  Kasthuri et al  kasthuri2014rule used a rule based MT system using transfer lexicon and morphological analysis tools  Anglabharathi was developed at IIT Kanpur, a system translating English to a collection of Indian languages including Tamil using CFG like structures to create a pseudo target to convert to Indian languages BIBREF17 , BIBREF18   A variety of hybrid approaches have also been used for English–Tamil MT in combinations of rule based (transfer methods), interlingua representations BIBREF19 , BIBREF20 , BIBREF21   The use of Statistical Machine Translation took over the English–Tamil MT system research because of its desirable properties of language independence, better generalization features and a reduced requirement of linguistic expertise BIBREF1 , BIBREF22 , BIBREF23   Various enhancement techniques external to the MT system have also been proposed to improve the performance of translation using morphological pre and post processing techniques BIBREF24 , BIBREF25 , BIBREF26  
The use of RNN Encoder Decoder models in machine translation has shown good results in languages with similar grammatical structure  Deep MT systems have been performing better than the other shallow SMT models recently, with the availability of computational resources and hardware making it feasible to train such models  The first of these models came in 2014, with Cho et al SecondOneByCho  The model used was the RNN LSTM encoder decoder model with the context vector output of the encoder (run for every word in the sentence) is fed to every decoder unit along with the previous word output until INLINEFORM0 EOS INLINEFORM1 is reached  This model was used to score translation results of another MT system  Sutskever et al  sutskever2014sequence created a similar encoder decoder model with the decoder getting the context vector only for the first word of the target language sentence  After that, only the decoded target outputs act as inputs to the various time steps of the decoder  One major drawback of these models is the size of the context vector of the encoder being static in nature  The same sized vector was expected to to represent sentences of arbitrary length, which was impractical when it came to very long sentences 
The next breakthrough came from Bahdanau et al  Bahdanau2014 where variable length word vectors were used and instead of just the context vector, a weighted sum of the inputs is given for the decoder  This enabled selective lookup to the source sentence during decoding and is known as the attention mechanism BIBREF27   The attention mechanism was further analysed by Luong et al  luong2015effective where they made a distinction between global and local attention by means of AER scores of the attention vectors  A Gaussian distribution and a monotonic lookup were used to facilitate the corresponding local source sentence look-up 
Conclusion
Thus, it is seen that the use of morphological segmentation on a morphologically rich language before translation helps with the performance of the translation in multiple ways  Thus, machine translation involving morphologically rich languages should ideally be carried out only after morphological segmentation  If the translation has to be carried out between two morphologically rich languages, then both the languages' sentences should be individually segmented based on morphology  This is because while it is true that they are both morphologically rich languages, the schemes that the languages use for the process of agglutination might be different, in which case a mapping between the units would be difficult without the segmentation 
One drawback of morphological segmentation is the increase in complexity of the model due to an increase in the average sentence lengths  This cannot be avoided as it is essential to enable a correspondence between the sentences of the two languages when one of them is a simple fusional language  Even with the increase in the average sentence length, the attention models that have been developed to ensure correctness of translation of long sequences can be put to good use when involving morphologically rich languages  Another point to note here is that morphologically rich languages like Tamil generally have lesser number of words per sentence than languages like English due to the inherent property of agglutination 
Future Work
The model implemented in this paper only includes source-side morphological segmentation and does not include a target side morphological agglutination to give back the output in words rather than morphemes  In order to implement an end-to-end translation system for morphologically rich languages, a morphological generator is essential because the output units of the translation cannot be morphemes 
The same model implemented can be further enhanced by means of a better corpus that can generalize over more than just domain specific source sentences  Also, the use of a better GPU would result in a better allocation of the hidden layer sizes and the batch sizes thereby possibly increasing the scope and accuracy of learning of the translation model 
Although not directly related to Machine Translation, the novel encoder– decoder architecture proposed in by Rocktaschel et al  rocktaschel2015reasoning for Natural Language Inference (NLI) can be used for the same  Their model fuses inferences from each and every individual word, summarizing information at each step, thereby linking the hidden state of the encoder with that of the decoder by means of a weighted sum, trained for optimization 
Acknowledgements
I would like to thank Dr  M  Anand Kumar, Assistant Professor, Amrita Vishwa Vidyapeetham for his continuous support and guidance  I would also like to thank Dr  Arvindan, Professor, SSN College Of Engineering for his inputs and suggestions 
Context 3: 
Experimental Setup ::: Summarization Datasets ::: CNN/DailyMail
contains news articles and associated highlights, i e , a few bullet points giving a brief overview of the article  We used the standard splits of BIBREF24 for training, validation, and testing (90,266/1,220/1,093 CNN documents and 196,961/12,148/10,397 DailyMail documents)  We did not anonymize entities  We first split sentences with the Stanford CoreNLP toolkit BIBREF26 and pre-processed the dataset following BIBREF6  Input documents were truncated to 512 tokens 
Experimental Setup ::: Summarization Datasets ::: NYT
contains 110,540 articles with abstractive summaries  Following BIBREF27, we split these into 100,834/9,706 training/test examples, based on the date of publication (the test set contains all articles published from January 1, 2007 onward)  We used 4,000 examples from the training as validation set  We also followed their filtering procedure, documents with summaries less than 50 words were removed from the dataset  The filtered test set (NYT50) includes 3,452 examples  Sentences were split with the Stanford CoreNLP toolkit BIBREF26 and pre-processed following BIBREF27  Input documents were truncated to 800 tokens 
Experimental Setup ::: Summarization Datasets ::: XSum
contains 226,711 news articles accompanied with a one-sentence summary, answering the question “What is this article about?”  We used the splits of BIBREF22 for training, validation, and testing (204,045/11,332/11,334) and followed the pre-processing introduced in their work  Input documents were truncated to 512 tokens 
Aside from various statistics on the three datasets, Table TABREF12 also reports the proportion of novel bi-grams in gold summaries as a measure of their abstractiveness  We would expect models with extractive biases to perform better on datasets with (mostly) extractive summaries, and abstractive models to perform more rewrite operations on datasets with abstractive summaries  CNN/DailyMail and NYT are somewhat abstractive, while XSum is highly abstractive 
Experimental Setup ::: Implementation Details
For both extractive and abstractive settings, we used PyTorch, OpenNMT BIBREF28 and the `bert-base-uncased' version of Bert to implement BertSum  Both source and target texts were tokenized with Bert's subwords tokenizer 
Experimental Setup ::: Implementation Details ::: Extractive Summarization
All extractive models were trained for 50,000 steps on 3 GPUs (GTX 1080 Ti) with gradient accumulation every two steps  Model checkpoints were saved and evaluated on the validation set every 1,000 steps  We selected the top-3 checkpoints based on the evaluation loss on the validation set, and report the averaged results on the test set  We used a greedy algorithm similar to BIBREF7 to obtain an oracle summary for each document to train extractive models  The algorithm generates an oracle consisting of multiple sentences which maximize the ROUGE-2 score against the gold summary 
When predicting summaries for a new document, we first use the model to obtain the score for each sentence  We then rank these sentences by their scores from highest to lowest, and select the top-3 sentences as the summary 
During sentence selection we use Trigram Blocking to reduce redundancy BIBREF9  Given summary $S$ and candidate sentence $c$, we skip $c$ if there exists a trigram overlapping between $c$ and $S$  The intuition is similar to Maximal Marginal Relevance (MMR; BIBREF29); we wish to minimize the similarity between the sentence being considered and sentences which have been already selected as part of the summary 
Experimental Setup ::: Implementation Details ::: Abstractive Summarization
In all abstractive models, we applied dropout (with probability $0 1$) before all linear layers; label smoothing BIBREF30 with smoothing factor $0 1$ was also used  Our Transformer decoder has 768 hidden units and the hidden size for all feed-forward layers is 2,048  All models were trained for 200,000 steps on 4 GPUs (GTX 1080 Ti) with gradient accumulation every five steps  Model checkpoints were saved and evaluated on the validation set every 2,500 steps  We selected the top-3 checkpoints based on their evaluation loss on the validation set, and report the averaged results on the test set 
During decoding we used beam search (size 5), and tuned the $\alpha $ for the length penalty BIBREF31 between $0 6$ and 1 on the validation set; we decode until an end-of-sequence token is emitted and repeated trigrams are blocked BIBREF9  It is worth noting that our decoder applies neither a copy nor a coverage mechanism BIBREF6, despite their popularity in abstractive summarization  This is mainly because we focus on building a minimum-requirements model and these mechanisms may introduce additional hyper-parameters to tune  Thanks to the subwords tokenizer, we also rarely observe issues with out-of-vocabulary words in the output; moreover, trigram-blocking produces diverse summaries managing to reduce repetitions 
Results ::: Automatic Evaluation
We evaluated summarization quality automatically using ROUGE BIBREF32  We report unigram and bigram overlap (ROUGE-1 and ROUGE-2) as a means of assessing informativeness and the longest common subsequence (ROUGE-L) as a means of assessing fluency  Table TABREF23 summarizes our results on the CNN/DailyMail dataset  The first block in the table includes the results of an extractive Oracle system as an upper bound  We also present the Lead-3 baseline (which simply selects the first three sentences in a document)  The second block in the table includes various extractive models trained on the CNN/DailyMail dataset (see Section SECREF5 for an overview)  For comparison to our own model, we also implemented a non-pretrained Transformer baseline (TransformerExt) which uses the same architecture as BertSumExt, but with fewer parameters  It is randomly initialized and only trained on the summarization task  TransformerExt has 6 layers, the hidden size is 512, and the feed-forward filter size is 2,048  The model was trained with same settings as in BIBREF3  The third block in Table TABREF23 highlights the performance of several abstractive models on the CNN/DailyMail dataset (see Section SECREF6 for an overview)  We also include an abstractive Transformer baseline (TransformerAbs) which has the same decoder as our abstractive BertSum models; the encoder is a 6-layer Transformer with 768 hidden size and 2,048 feed-forward filter size  The fourth block reports results with fine-tuned Bert models: BertSumExt and its two variants (one without interval embeddings, and one with the large version of Bert), BertSumAbs, and BertSumExtAbs  Bert-based models outperform the Lead-3 baseline which is not a strawman; on the CNN/DailyMail corpus it is indeed superior to several extractive BIBREF7, BIBREF8, BIBREF19 and abstractive models BIBREF6  Bert models collectively outperform all previously proposed extractive and abstractive systems, only falling behind the Oracle upper bound  Among Bert variants, BertSumExt performs best which is not entirely surprising; CNN/DailyMail summaries are somewhat extractive and even abstractive models are prone to copying sentences from the source document when trained on this dataset BIBREF6  Perhaps unsurprisingly we observe that larger versions of Bert lead to performance improvements and that interval embeddings bring only slight gains  Table TABREF24 presents results on the NYT dataset  Following the evaluation protocol in BIBREF27, we use limited-length ROUGE Recall, where predicted summaries are truncated to the length of the gold summaries  Again, we report the performance of the Oracle upper bound and Lead-3 baseline  The second block in the table contains previously proposed extractive models as well as our own Transformer baseline  Compress BIBREF27 is an ILP-based model which combines compression and anaphoricity constraints  The third block includes abstractive models from the literature, and our Transformer baseline  Bert-based models are shown in the fourth block  Again, we observe that they outperform previously proposed approaches  On this dataset, abstractive Bert models generally perform better compared to BertSumExt, almost approaching Oracle performance 
Table TABREF26 summarizes our results on the XSum dataset  Recall that summaries in this dataset are highly abstractive (see Table TABREF12) consisting of a single sentence conveying the gist of the document  Extractive models here perform poorly as corroborated by the low performance of the Lead baseline (which simply selects the leading sentence from the document), and the Oracle (which selects a single-best sentence in each document) in Table TABREF26  As a result, we do not report results for extractive models on this dataset  The second block in Table TABREF26 presents the results of various abstractive models taken from BIBREF22 and also includes our own abstractive Transformer baseline  In the third block we show the results of our Bert summarizers which again are superior to all previously reported models (by a wide margin) 
Results ::: Model Analysis ::: Learning Rates
Recall that our abstractive model uses separate optimizers for the encoder and decoder  In Table TABREF27 we examine whether the combination of different learning rates ($\tilde{lr}_{\mathcal {E}}$ and $\tilde{lr}_{\mathcal {D}}$) is indeed beneficial  Specifically, we report model perplexity on the CNN/DailyMail validation set for varying encoder/decoder learning rates  We can see that the model performs best with $\tilde{lr}_{\mathcal {E}}=2e-3$ and $\tilde{lr}_{\mathcal {D}}=0 1$ 
Results ::: Model Analysis ::: Position of Extracted Sentences
In addition to the evaluation based on ROUGE, we also analyzed in more detail the summaries produced by our model  For the extractive setting, we looked at the position (in the source document) of the sentences which were selected to appear in the summary  Figure FIGREF31 shows the proportion of selected summary sentences which appear in the source document at positions 1, 2, and so on  The analysis was conducted on the CNN/DailyMail dataset for Oracle summaries, and those produced by BertSumExt and the TransformerExt  We can see that Oracle summary sentences are fairly smoothly distributed across documents, while summaries created by TransformerExt mostly concentrate on the first document sentences  BertSumExt outputs are more similar to Oracle summaries, indicating that with the pretrained encoder, the model relies less on shallow position features, and learns deeper document representations 
Results ::: Model Analysis ::: Novel N-grams
We also analyzed the output of abstractive systems by calculating the proportion of novel n-grams that appear in the summaries but not in the source texts  The results are shown in Figure FIGREF33  In the CNN/DailyMail dataset, the proportion of novel n-grams in automatically generated summaries is much lower compared to reference summaries, but in XSum, this gap is much smaller  We also observe that on CNN/DailyMail, BertExtAbs produces less novel n-ngrams than BertAbs, which is not surprising  BertExtAbs is more biased towards selecting sentences from the source document since it is initially trained as an extractive model  The supplementary material includes examples of system output and additional ablation studies 
Results ::: Human Evaluation
In addition to automatic evaluation, we also evaluated system output by eliciting human judgments  We report experiments following a question-answering (QA) paradigm BIBREF33, BIBREF8 which quantifies the degree to which summarization models retain key information from the document  Under this paradigm, a set of questions is created based on the gold summary under the assumption that it highlights the most important document content  Participants are then asked to answer these questions by reading system summaries alone without access to the article  The more questions a system can answer, the better it is at summarizing the document as a whole  Moreover, we also assessed the overall quality of the summaries produced by abstractive systems which due to their ability to rewrite content may produce disfluent or ungrammatical output  Specifically, we followed the Best-Worst Scaling BIBREF34 method where participants were presented with the output of two systems (and the original document) and asked to decide which one was better according to the criteria of Informativeness, Fluency, and Succinctness 
Both types of evaluation were conducted on the Amazon Mechanical Turk platform  For the CNN/DailyMail and NYT datasets we used the same documents (20 in total) and questions from previous work BIBREF8, BIBREF18  For XSum, we randomly selected 20 documents (and their questions) from the release of BIBREF22  We elicited 3 responses per HIT  With regard to QA evaluation, we adopted the scoring mechanism from BIBREF33; correct answers were marked with a score of one, partially correct answers with 0 5, and zero otherwise  For quality-based evaluation, the rating of each system was computed as the percentage of times it was chosen as better minus the times it was selected as worse  Ratings thus range from -1 (worst) to 1 (best) 
Results for extractive and abstractive systems are shown in Tables TABREF37 and TABREF38, respectively  We compared the best performing BertSum model in each setting (extractive or abstractive) against various state-of-the-art systems (whose output is publicly available), the Lead baseline, and the Gold standard as an upper bound  As shown in both tables participants overwhelmingly prefer the output of our model against comparison systems across datasets and evaluation paradigms  All differences between BertSum and comparison models are statistically significant ($p<0 05$), with the exception of TConvS2S (see Table TABREF38; XSum) in the QA evaluation setting 
Conclusions
In this paper, we showcased how pretrained Bert can be usefully applied in text summarization  We introduced a novel document-level encoder and proposed a general framework for both abstractive and extractive summarization  Experimental results across three datasets show that our model achieves state-of-the-art results across the board under automatic and human-based evaluation protocols  Although we mainly focused on document encoding for summarization, in the future, we would like to take advantage the capabilities of Bert for language generation 
Acknowledgments
This research is supported by a Google PhD Fellowship to the first author  We gratefully acknowledge the support of the European Research Council (Lapata, award number 681760, “Translating Multiple Modalities into Text”)  We would also like to thank Shashi Narayan for providing us with the XSum dataset 

ground_truth: 50 human annotators ranked a random sample of 100 translations by Adequacy, Fluency and overall ranking on a 5-point scale.

adequacy, precision and ranking values

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What are the datasets used for evaluation?
Context 4:  Equation (DISPLAY_FORM13) can ensure the Gaussian weight equals 1 when $dis_{ij}$ is 0  The larger distance between charactersis, the smaller the weight is, which makes one character can affect its adjacent characters more compared with other characters 
To combine the Gaussian weight to the self-attention, we produce the Hadamard product of Gaussian weight matrix $G$ and the score matrix produced by $Q{K^{T}}$
where $AG$ is the Gaussian-masked attention  It ensures that the relationship between two characters with long distances is weaker than adjacent characters 
The scaled dot-product attention models the relationship between two characters without regard to their distances in one sequence  For CWS task, the weight between adjacent characters should be more important while it is hard for self-attention to achieve the effect explicitly because the self-attention cannot get the order of sentences directly  The Gaussian-masked attention adjusts the weight between characters and their adjacent character to a larger value which stands for the effect of adjacent characters 
For forward and backward encoder, the self-attention sublayer needs to use a triangular matrix mask to let the self-attention focus on different weights:
where $pos_i$ is the position of character $c_i$  The triangular matrix for forward and backward encode are:
$\left[ \begin{matrix} 1 & 0 & 0 & \cdots &0\\ 1 & 1 & 0 & \cdots &0\\ 1 & 1 & 1 & \cdots &0\\ \vdots &\vdots &\vdots &\ddots &\vdots \\ 1 & 1 & 1 & \cdots & 1\\ \end{matrix} \right]$ $\left[ \begin{matrix} 1 & 1 & 1 & \cdots &1 \\ 0 & 1 & 1 & \cdots &1 \\ 0 & 0& 1 & \cdots &1 \\ \vdots &\vdots &\vdots &\ddots &\vdots \\ 0 & 0 & 0 & \cdots & 1\\ \end{matrix}\right]$
Similar as BIBREF24, we use multi-head attention to capture information from different dimension positions as Figure FIGREF16 and get Gaussian-masked directional multi-head attention  With multi-head attention architecture, the representation of input can be captured by
where $MH$ is the Gaussian-masked multi-head attention, ${W_i^q, W_i^k,W_i^v} \in \mathbb {R}^{d_k \times d_h}$ is the parameter matrices to generate heads, $d_k$ is the dimension of model and $d_h$ is the dimension of one head 
Models ::: Bi-affinal Attention Scorer
Regarding word boundaries as gaps between any adjacent words converts the character labeling task to the gap labeling task  Different from character labeling task, gap labeling task requires information of two adjacent characters  The relationship between adjacent characters can be represented as the type of gap  The characteristic of word boundaries makes bi-affine attention an appropriate scorer for CWS task 
Bi-affinal attention scorer is the component that we use to label the gap  Bi-affinal attention is developed from bilinear attention which has been used in dependency parsing BIBREF26 and SRL BIBREF27  The distribution of labels in a labeling task is often uneven which makes the output layer often include a fixed bias term for the prior probability of different labels BIBREF27  Bi-affine attention uses bias terms to alleviate the burden of the fixed bias term and get the prior probability which makes it different from bilinear attention  The distribution of the gap is uneven that is similar as other labeling task which fits bi-affine 
Bi-affinal attention scorer labels the target depending on information of independent unit and the joint information of two units  In bi-affinal attention, the score $s_{ij}$ of characters $c_i$ and $c_j$ $(i < j)$ is calculated by:
where $v_i^f$ is the forward information of $c_i$ and $v_i^b$ is the backward information of $c_j$  In Equation (DISPLAY_FORM21), $W$, $U$ and $b$ are all parameters that can be updated in training  $W$ is a matrix with shape $(d_i \times N\times d_j)$ and $U$ is a $(N\times (d_i + d_j))$ matrix where $d_i$ is the dimension of vector $v_i^f$ and $N$ is the number of labels 
In our model, the biaffine scorer uses the forward information of character in front of the gap and the backward information of the character behind the gap to distinguish the position of characters  Figure FIGREF22 is an example of labeling gap  The method of using biaffine scorer ensures that the boundaries of words can be determined by adjacent characters with different directional information  The score vector of the gap is formed by the probability of being a boundary of word  Further, the model generates all boundaries using activation function in a greedy decoding way 
Experiments ::: Experimental Settings ::: Data
We train and evaluate our model on datasets from SIGHAN Bakeoff 2005 BIBREF21 which has four datasets, PKU, MSR, AS and CITYU  Table TABREF23 shows the statistics of train data  We use F-score to evaluate CWS models  To train model with pre-trained embeddings in AS and CITYU, we use OpenCC to transfer data from traditional Chinese to simplified Chinese 
Experiments ::: Experimental Settings ::: Pre-trained Embedding
We only use unigram feature so we only trained character embeddings  Our pre-trained embedding are pre-trained on Chinese Wikipedia corpus by word2vec BIBREF29 toolkit  The corpus used for pre-trained embedding is all transferred to simplified Chinese and not segmented  On closed test, we use embeddings initialized randomly 
Experiments ::: Experimental Settings ::: Hyperparameters
For different datasets, we use two kinds of hyperparameters which are presented in Table TABREF24  We use hyperparameters in Table TABREF24 for small corpora (PKU and CITYU) and normal corpora (MSR and AS)  We set the standard deviation of Gaussian function in Equation (DISPLAY_FORM13) to 2  Each training batch contains sentences with at most 4096 tokens 
Experiments ::: Experimental Settings ::: Optimizer
To train our model, we use the Adam BIBREF30 optimizer with $\beta _1=0 9$, $\beta _2=0 98$ and $\epsilon =10^{-9}$  The learning rate schedule is the same as BIBREF24:
where $d$ is the dimension of embeddings, $step$ is the step number of training and $warmup_step$ is the step number of warmup  When the number of steps is smaller than the step of warmup, the learning rate increases linearly and then decreases 
Experiments ::: Hardware and Implements
We trained our models on a single CPU (Intel i7-5960X) with an nVidia 1080 Ti GPU  We implement our model in Python with Pytorch 1 0 
Experiments ::: Results
Tables TABREF25 and TABREF26 reports the performance of recent models and ours in terms of closed test setting  Without the assistance of unsupervised segmentation features userd in BIBREF20, our model outperforms all the other models in MSR and AS except BIBREF18 and get comparable performance in PKU and CITYU  Note that all the other models for this comparison adopt various $n$-gram features while only our model takes unigram ones 
With unsupervised segmentation features introduced by BIBREF20, our model gets a higher result  Specially, the results in MSR and AS achieve new state-of-the-art and approaching previous state-of-the-art in CITYU and PKU  The unsupervised segmentation features are derived from the given training dataset, thus using them does not violate the rule of closed test of SIGHAN Bakeoff 
Table TABREF36 compares our model and recent neural models in terms of open test setting in which any external resources, especially pre-trained embeddings or language models can be used  In MSR and AS, our model gets a comparable result while our results in CITYU and PKU are not remarkable 
However, it is well known that it is always hard to compare models when using open test setting, especially with pre-trained embedding  Not all models may use the same method and data to pre-train  Though pre-trained embedding or language model can improve the performance, the performance improvement itself may be from multiple sources  It often that there is a success of pre-trained embedding to improve the performance, while it cannot prove that the model is better 
Compared with other LSTM models, our model performs better in AS and MSR than in CITYU and PKU  Considering the scale of different corpora, we believe that the size of corpus affects our model and the larger size is, the better model performs  For small corpus, the model tends to be overfitting 
Tables TABREF25 and TABREF26 also show the decoding time in different datasets  Our model finishes the segmentation with the least decoding time in all four datasets, thanks to the architecture of model which only takes attention mechanism as basic block 
Related Work ::: Chinese Word Segmentation
CWS is a task for Chinese natural language process to delimit word boundary  BIBREF0 for the first time formulize CWS as a sequence labeling task  BIBREF3 show that different character tag sets can make essential impact for CWS  BIBREF2 use CRFs as a model for CWS, achieving new state-of-the-art  Works of statistical CWS has built the basis for neural CWS 
Neural word segmentation has been widely used to minimize the efforts in feature engineering which was important in statistical CWS  BIBREF4 introduce the neural model with sliding-window based sequence labeling  BIBREF6 propose a gated recursive neural network (GRNN) for CWS to incorporate complicated combination of contextual character and n-gram features  BIBREF7 use LSTM to learn long distance information  BIBREF9 propose a neural framework that eliminates context windows and utilize complete segmentation history  BIBREF33 explore a joint model that performs segmentation, POS-Tagging and chunking simultaneously  BIBREF34 propose a feature-enriched neural model for joint CWS and part-of-speech tagging  BIBREF35 present a joint model to enhance the segmentation of Chinese microtext by performing CWS and informal word detection simultaneously  BIBREF17 propose a character-based convolutional neural model to capture $n$-gram features automatically and an effective approach to incorporate word embeddings  BIBREF11 improve the model in BIBREF9 and propose a greedy neural word segmenter with balanced word and character embedding inputs  BIBREF23 propose a novel neural network model to incorporate unlabeled and partially-labeled data  BIBREF36 propose two methods that extend the Bi-LSTM to perform incorporating dictionaries into neural networks for CWS  BIBREF37 propose Switch-LSTMs to segment words and provided a more flexible solution for multi-criteria CWS which is easy to transfer the learned knowledge to new criteria 
Related Work ::: Transformer
Transformer BIBREF24 is an attention-based neural machine translation model  The Transformer is one kind of self-attention networks (SANs) which is proposed in BIBREF38  Encoder of the Transformer consists of one self-attention layer and a position-wise feed-forward layer  Decoder of the Transformer contains one self-attention layer, one encoder-decoder attention layer and one position-wise feed-forward layer  The Transformer uses residual connections around the sublayers and then followed by a layer normalization layer 
Scaled dot-product attention is the key component in the Transformer  The input of attention contains queries, keys, and values of input sequences  The attention is generated using queries and keys like Equation (DISPLAY_FORM11)  Structure of scaled dot-product attention allows the self-attention layer generate the representation of sentences at once and contain the information of the sentence which is different from RNN that process characters of sentences one by one  Standard self-attention is similar as Gaussian-masked direction attention while it does not have directional mask and gaussian mask  BIBREF24 also propose multi-head attention which is better to generate representation of sentence by dividing queries, keys and values to different heads and get information from different subspaces 
Conclusion
In this paper, we propose an attention mechanism only based Chinese word segmentation model  Our model uses self-attention from the Transformer encoder to take sequence input and bi-affine attention scorer to predict the label of gaps  To improve the ability of capturing the localness and directional information of self-attention based encoder, we propose a variant of self-attention called Gaussian-masked directional multi-head attention to replace the standard self-attention  We also extend the Transformer encoder to capture directional features  Our model uses only unigram features instead of multiple $n$-gram features in previous work  Our model is evaluated on standard benchmark dataset, SIGHAN Bakeoff 2005, which shows not only our model performs segmentation faster than any previous models but also gives new higher or comparable segmentation performance against previous state-of-the-art models 

ground_truth: CNN/DailyMail news highlights, New York Times Annotated Corpus, XSum

the CNN/DailyMail news highlights dataset BIBREF24, the New York Times Annotated Corpus (NYT; BIBREF25), XSum BIBREF22