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. Which eight NER tasks did they evaluate on?
Context 1: Introduction
Pretrained Language Models (PTLMs) such as BERT BIBREF1 have spearheaded advances on many NLP tasks  Usually, PTLMs are pretrained on unlabeled general-domain and/or mixed-domain text, such as Wikipedia, digital books or the Common Crawl corpus 
When applying PTLMs to specific domains, it can be useful to domain-adapt them  Domain adaptation of PTLMs has typically been achieved by pretraining on target-domain text  One such model is BioBERT BIBREF2, which was initialized from general-domain BERT and then pretrained on biomedical scientific publications  The domain adaptation is shown to be helpful for target-domain tasks such as biomedical Named Entity Recognition (NER) or Question Answering (QA)  On the downside, the computational cost of pretraining can be considerable: BioBERTv1 0 was adapted for ten days on eight large GPUs (see Table TABREF1), which is expensive, environmentally unfriendly, prohibitive for small research labs and students, and may delay prototyping on emerging domains 
We therefore propose a fast, CPU-only domain-adaptation method for PTLMs: We train Word2Vec BIBREF3 on target-domain text and align the resulting word vectors with the wordpiece vectors of an existing general-domain PTLM  The PTLM thus gains domain-specific lexical knowledge in the form of additional word vectors, but its deeper layers remain unchanged  Since Word2Vec and the vector space alignment are efficient models, the process requires a fraction of the resources associated with pretraining the PTLM itself, and it can be done on CPU 
In Section SECREF4, we use the proposed method to domain-adapt BERT on PubMed+PMC (the data used for BioBERTv1 0) and/or CORD-19 (Covid-19 Open Research Dataset)  We improve over general-domain BERT on eight out of eight biomedical NER tasks, using a fraction of the compute cost associated with BioBERT  In Section SECREF5, we show how to quickly adapt an existing Question Answering model to text about the Covid-19 pandemic, without any target-domain Language Model pretraining or finetuning 
Related work ::: The BERT PTLM
For our purpose, a PTLM consists of three parts: A tokenizer $\mathcal {T}_\mathrm {LM} : \mathbb {L}^+ \rightarrow \mathbb {L}_\mathrm {LM}^+$, a wordpiece embedding function $\mathcal {E}_\mathrm {LM}: \mathbb {L}_\mathrm {LM} \rightarrow \mathbb {R}^{d_\mathrm {LM}}$ and an encoder function $\mathcal {F}_\mathrm {LM}$  $\mathbb {L}_\mathrm {LM}$ is a limited vocabulary of wordpieces  All words that are not in $\mathbb {L}_\mathrm {LM}$ are tokenized into sequences of shorter wordpieces, e g , tachycardia becomes ta ##chy ##card ##ia  Given a sentence $S = [w_1, \ldots , w_T]$, tokenized as $\mathcal {T}_\mathrm {LM}(S) = [\mathcal {T}_\mathrm {LM}(w_1); \ldots ; \mathcal {T}_\mathrm {LM}(w_T)]$, $\mathcal {E}_\mathrm {LM}$ embeds every wordpiece in $\mathcal {T}_\mathrm {LM}(S)$ into a real-valued, trainable wordpiece vector  The wordpiece vectors of the entire sequence are stacked and fed into $\mathcal {F}_\mathrm {LM}$  Note that we consider position and segment embeddings to be a part of $\mathcal {F}_\mathrm {LM}$ rather than $\mathcal {E}_\mathrm {LM}$ 
In the case of BERT, $\mathcal {F}_\mathrm {LM}$ is a Transformer BIBREF4, followed by a final Feed-Forward Net  During pretraining, the Feed-Forward Net predicts the identity of masked wordpieces  When finetuning on a supervised task, it is usually replaced with a randomly initialized task-specific layer 
Related work ::: Domain-adapted PTLMs
Domain adaptation of PTLMs is typically achieved by pretraining on unlabeled target-domain text  Some examples of such models are BioBERT BIBREF2, which was pretrained on the PubMed and/or PubMed Central (PMC) corpora, SciBERT BIBREF5, which was pretrained on papers from SemanticScholar, ClinicalBERT BIBREF6, BIBREF7 and ClinicalXLNet BIBREF8, which were pretrained on clinical patient notes, and AdaptaBERT BIBREF9, which was pretrained on Early Modern English text  In most cases, a domain-adapted PTLM is initialized from a general-domain PTLM (e g , standard BERT), though BIBREF5 report better results with a model that was pretrained from scratch with a custom wordpiece vocabulary  In this paper, we focus on BioBERT, as its domain adaptation corpora are publicly available 
Related work ::: Word vectors
Word vectors are distributed representations of words that are trained on unlabeled text  Contrary to PTLMs, word vectors are non-contextual, i e , a word type is always assigned the same vector, regardless of context  In this paper, we use Word2Vec BIBREF3 to train word vectors  We will denote the Word2Vec lookup function as $\mathcal {E}_\mathrm {W2V} : \mathbb {L}_\mathrm {W2V} \rightarrow \mathbb {R}^{d_\mathrm {W2V}}$ 
Related work ::: Word vector space alignment
Word vector space alignment has most frequently been explored in the context of cross-lingual word embeddings  For instance, BIBREF10 align English and Spanish Word2Vec spaces by a simple linear transformation  BIBREF11 use a related method to align cross-lingual word vectors and multilingual BERT wordpiece vectors 
Method
In the following, we assume access to a general-domain PTLM, as described in Section SECREF2, and a corpus of unlabeled target-domain text 
Method ::: Creating new input vectors
In a first step, we train Word2Vec on the target-domain corpus  In a second step, we take the intersection of $\mathbb {L}_\mathrm {LM}$ and $\mathbb {L}_\mathrm {W2V}$  In practice, the intersection mostly contains wordpieces from $\mathbb {L}_\mathrm {LM}$ that correspond to standalone words  It also contains single characters and other noise, however, we found that filtering them does not improve alignment quality  In a third step, we use the intersection to fit an unconstrained linear transformation $\mathbf {W} \in \mathbb {R}^{d_\mathrm {LM} \times d_\mathrm {W2V}}$ via least squares:
Intuitively, $\mathbf {W}$ makes Word2Vec vectors “look like” the PTLM's native wordpiece vectors, just like cross-lingual alignment makes word vectors from one language “look like” word vectors from another language  In Table TABREF7 (top), we show examples of within-space and cross-space nearest neighbors after alignment 
Method ::: Updating the wordpiece embedding layer
Next, we redefine the wordpiece embedding layer of the PTLM  The most radical strategy would be to replace the entire layer with the aligned Word2Vec vectors:
In initial experiments, this strategy led to a drop in performance, presumably because function words are not well represented by Word2Vec, and replacing them disrupts BERT's syntactic abilities  To prevent this problem, we leave existing wordpiece vectors intact and only add new ones:
Method ::: Updating the tokenizer
In a final step, we update the tokenizer to account for the added words  Let $\mathcal {T}_\mathrm {LM}$ be the standard BERT tokenizer, and let $\hat{\mathcal {T}}_\mathrm {LM}$ be the tokenizer that treats all words in $\mathbb {L}_\mathrm {LM} \cup \mathbb {L}_\mathrm {W2V}$ as one-wordpiece tokens, while tokenizing any other words as usual 
In practice, a given word may or may not benefit from being tokenized by $\hat{\mathcal {T}}_\mathrm {LM}$ instead of $\mathcal {T}_\mathrm {LM}$  To give a concrete example, 82% of the words in the BC5CDR NER dataset that end in the suffix -ia are inside a disease entity (e g , tachycardia)  $\mathcal {T}_\mathrm {LM}$ tokenizes this word as ta ##chy ##card ##ia, thereby exposing the orthographic cue to the model  As a result, $\mathcal {T}_\mathrm {LM}$ leads to higher recall on -ia diseases  But there are many cases where wordpiece tokenization is meaningless or misleading  For instance euthymia (not a disease) is tokenized by $\mathcal {T}_\mathrm {LM}$ as e ##uth ##ym ##ia, making it likely to be classified as a disease  By contrast, $\hat{\mathcal {T}}_\mathrm {LM}$ gives euthymia a one-wordpiece representation that depends only on distributional semantics  We find that using $\hat{\mathcal {T}}_\mathrm {LM}$ improves precision on -ia diseases 
To combine these complementary strengths, we use a 50/50 mixture of $\mathcal {T}_\mathrm {LM}$-tokenization and $\hat{\mathcal {T}}_\mathrm {LM}$-tokenization when finetuning the PTLM on a task  At test time, we use both tokenizers and mean-pool the outputs  Let $o(\mathcal {T}(S))$ be some output of interest (e g , a logit), given sentence $S$ tokenized by $\mathcal {T}$  We predict:
Experiment 1: Biomedical NER
In this section, we use the proposed method to create GreenBioBERT, an inexpensive and environmentally friendly alternative to BioBERT  Recall that BioBERTv1 0 (biobert_v1 0_pubmed_pmc) was initialized from general-domain BERT (bert-base-cased) and pretrained on PubMed+PMC 
Experiment 1: Biomedical NER ::: Domain adaptation
We train Word2Vec with vector size $d_\mathrm {W2V} = d_\mathrm {LM} = 768$ on PubMed+PMC (see Appendix for details)  Then, we follow the procedure described in Section SECREF3 to update the wordpiece embedding layer and tokenizer of general-domain BERT 
Experiment 1: Biomedical NER ::: Finetuning
We finetune GreenBioBERT on the eight publicly available NER tasks used in BIBREF2  We also do reproduction experiments with general-domain BERT and BioBERTv1 0, using the same setup as our model  We average results over eight random seeds  See Appendix for details on preprocessing, training and hyperparameters 
Experiment 1: Biomedical NER ::: Results and discussion
Table TABREF7 (bottom) shows entity-level precision, recall and F1  For ease of visualization, Figure FIGREF13 shows what portion of the BioBERT – BERT F1 delta is covered  We improve over general-domain BERT on all tasks with varying effect sizes  Depending on the points of reference, we cover an average 52% to 60% of the BioBERT – BERT F1 delta (54% for BioBERTv1 0, 60% for BioBERTv1 1 and 52% for our reproduction experiments)  Table TABREF17 (top) shows the importance of vector space alignment: If we replace the aligned Word2Vec vectors with their non-aligned counterparts (by setting $\mathbf {W} = \mathbf {1}$) or with randomly initialized vectors, F1 drops on all tasks 
Experiment 2: Covid-19 QA
In this section, we use the proposed method to quickly adapt an existing general-domain QA model to an emerging target domain: Covid-19  Our baseline model is SQuADBERT (bert-large-uncased-whole-word-masking-finetuned-squad), a version of BERT that was finetuned on general-domain SQuAD BIBREF19  We evaluate on Deepset-AI Covid-QA, a SQuAD-style dataset with 1380 questions (see Appendix for details on data and preprocessing)  We assume that there is no target-domain finetuning data, which is a realistic setup for a new domain 
Experiment 2: Covid-19 QA ::: Domain adaptation
We train Word2Vec with vector size $d_\mathrm {W2V} = d_\mathrm {LM} = 1024$ on CORD-19 (Covid-19 Open Research Dataset) and/or PubMed+PMC  The process takes less than an hour on CORD-19 and about one day on the combined corpus, again without the need for a GPU  Then, we update SQuADBERT's wordpiece embedding layer and tokenizer, as described in Section SECREF3  We refer to the resulting model as GreenCovidSQuADBERT 
Experiment 2: Covid-19 QA ::: Results and discussion
Table TABREF17 (bottom) shows that GreenCovidSQuADBERT outperforms general-domain SQuADBERT in all metrics  Most of the improvement can be achieved with just the small CORD-19 corpus, which is more specific to the target domain (compare “Cord-19 only” and “Cord-19+PubMed+PMC”) 
Conclusion
As a reaction to the trend towards high-resource models, we have proposed an inexpensive, CPU-only method for domain-adapting Pretrained Language Models: We train Word2Vec vectors on target-domain data and align them with the wordpiece vector space of a general-domain PTLM 
On eight biomedical NER tasks, we cover over 50% of the BioBERT – BERT F1 delta, at 5% of BioBERT's domain adaptation CO$_2$ footprint and 2% of its cloud compute cost  We have also shown how to rapidly adapt an existing BERT QA model to an emerging domain – the Covid-19 pandemic – without the need for target-domain Language Model pretraining or finetuning 
We hope that our approach will benefit practitioners with limited time or resources, and that it will encourage environmentally friendlier NLP 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Word2Vec training
We downloaded the PubMed, PMC and CORD-19 corpora from:
https://ftp ncbi nlm nih gov/pub/pmc/oa_bulk/ [20 January 2020, 68GB raw text]
https://ftp ncbi nlm nih gov/pubmed/baseline/ [20 January 2020, 24GB raw text]
https://pages semanticscholar org/coronavirus-research [17 April 2020, 2GB raw text]
We extract all abstracts and text bodies and apply the BERT basic tokenizer (a word tokenizer that standard BERT uses before wordpiece tokenization)  Then, we train CBOW Word2Vec with negative sampling  We use default parameters except for the vector size (which we set to $d_\mathrm {W2V} = d_\mathrm {LM}$) 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Pretrained models
General-domain BERT and BioBERTv1 0 were downloaded from:
https://storage googleapis com/bert_models/2018_10_18/cased_L-12_H-768_A-12 zip
https://github com/naver/biobert-pretrained
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Data
We downloaded the NER datasets by following instructions on https://github com/dmis-lab/biobert#Datasets  For detailed dataset statistics, see BIBREF2 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Preprocessing
We cut all sentences into chunks of 30 or fewer whitespace-tokenized words (without splitting inside labeled spans)  Then, we tokenize every chunk $S$ with $\mathcal {T} = \mathcal {T}_\mathrm {LM}$ or $\mathcal {T} = \hat{\mathcal {T}}_\mathrm {LM}$ and add special tokens:
Word-initial wordpieces in $\mathcal {T}(S)$ are labeled as B(egin), I(nside) or O(utside), while non-word-initial wordpieces are labeled as X(ignore) 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Modeling, training and inference
We follow BIBREF2's implementation (https://github com/dmis-lab/biobert): We add a randomly initialized softmax classifier on top of the last BERT layer to predict the labels  We finetune the entire model to minimize negative log likelihood, with the standard Adam optimizer BIBREF20 and a linear learning rate scheduler (10% warmup)  Like BIBREF2, we finetune on the concatenation of the training and development set  All finetuning runs were done on a GeForce Titan X GPU (12GB) 
Since we do not have the resources for an extensive hyperparameter search, we use defaults and recommendations from the BioBERT repository: Batch size of 32, peak learning rate of $1 \cdot 10^{-5}$, and 100 epochs 
At inference time, we gather the output logits of word-initial wordpieces only  Since the number of word-initial wordpieces is the same for $\mathcal {T}_\mathrm {LM}(S)$ and $\hat{\mathcal {T}}_\mathrm {LM}(S)$, this makes mean-pooling the logits straightforward 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Note on our reproduction experiments
We found it easier to reproduce or exceed BIBREF2's results for general-domain BERT, compared to their results for BioBERTv1 0 (see Figure FIGREF13, main paper)  While this may be due to hyperparameters, it suggests that BioBERTv1 0 was more strongly tuned than BERT in the original BioBERT paper  This observation does not affect our conclusions, as GreenBioBERT performs better than reproduced BERT as well 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Pretrained model
We downloaded the SQuADBERT baseline from:
https://huggingface co/bert-large-uncased-whole-word-masking-finetuned-squad
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Data
We downloaded the Deepset-AI Covid-QA dataset from:
https://github com/deepset-ai/COVID-QA/blob/master/data/question-answering/200423_covidQA json [24 April 2020]
At the time of writing, the dataset contains 1380 questions and gold answer spans  Every question is associated with one of 98 research papers (contexts)  We treat the entire dataset as a test set 
Note that there are some important differences between the dataset and SQuAD, which make the task challenging:
The contexts are full documents rather than single paragraphs  Thus, the correct answer may appear several times, often with slightly different wordings  Only a single one of the occurrences is annotated as correct, e g :
What was the prevalence of Coronavirus OC43 in community samples in Ilorin, Nigeria?
13 3% (95% CI 6 9-23 6%) # from main text
(13 3%, 10/75)  # from abstract
SQuAD gold answers are defined as the “shortest span in the paragraph that answered the question” BIBREF19, but many Covid-QA gold answers are longer and contain non-essential context, e g :
When was the Middle East Respiratory Syndrome Coronavirus isolated first?
(MERS-CoV) was first isolated in 2012, in a 60-year-old man who died in Jeddah, KSA due to severe acute pneumonia and multiple organ failure
2012,
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Preprocessing
We tokenize every question-context pair $(Q, C)$ with $\mathcal {T} = \mathcal {T}_\mathrm {LM}$ or $\mathcal {T} = \hat{\mathcal {T}}_\mathrm {LM}$, which yields $(\mathcal {T}(Q), \mathcal {T}(C))$  Since $\mathcal {T}(C)$ is usually too long to be digested in a single forward pass, we define a sliding window with width and stride $N = \mathrm {floor}(\frac{509 - |\mathcal {T}(Q)|}{2})$  At step $n$, the “active” window is between $a^{(l)}_n = nN$ and $a^{(r)}_n = \mathrm {min}(|C|, nN+N)$  The input is defined as:
$p^{(l)}_n$ and $p^{(r)}_n$ are chosen such that $|X^{(n)}| = 512$, and such that the active window is in the center of the input (if possible) 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Modeling and inference
Feeding $X^{(n)}$ into the pretrained QA model yields start logits $\mathbf {h^{\prime }}^{(\mathrm {start}, n)} \in \mathbb {R}^{|X^{(n)}|}$ and end logits $\mathbf {h^{\prime }}^{(\mathrm {end},n)} \in \mathbb {R}^{|X^{(n)}|}$  We extract and concatenate the slices that correspond to the active windows of all steps:
Next, we map the logits from the wordpiece level to the word level  This allows us to mean-pool the outputs of $\mathcal {T}_\mathrm {LM}$ and $\hat{\mathcal {T}}_\mathrm {LM}$ even when $|\mathcal {T}_\mathrm {LM}(C)| \ne |\hat{\mathcal {T}}_\mathrm {LM}(C)|$ 
Let $c_i$ be a whitespace-delimited word in $C$  Let $\mathcal {T}(C)_{j:j+|\mathcal {T}(c_i)|}$ be the corresponding wordpieces  The start and end logits of $c_i$ are derived as:
Finally, we return the answer span $C_{k:k^{\prime }}$ that maximizes $o^{(\mathrm {start})}_k + o^{(\mathrm {end})}_{k^{\prime }}$, subject to the constraints that $k^{\prime }$ does not precede $k$ and the answer span is not longer than 500 characters 
Context 2: Introduction
Rendering natural language descriptions from structured data is required in a wide variety of commercial applications such as generating descriptions of products, hotels, furniture, etc , from a corresponding table of facts about the entity  Such a table typically contains {field, value} pairs where the field is a property of the entity (e g , color) and the value is a set of possible assignments to this property (e g , color = red)  Another example of this is the recently introduced task of generating one line biography descriptions from a given Wikipedia infobox BIBREF0   The Wikipedia infobox serves as a table of facts about a person and the first sentence from the corresponding article serves as a one line description of the person  Figure FIGREF2 illustrates an example input infobox which contains fields such as Born, Residence, Nationality, Fields, Institutions and Alma Mater  Each field further contains some words (e g , particle physics, many-body theory, etc )  The corresponding description is coherent with the information contained in the infobox 
Note that the number of fields in the infobox and the ordering of the fields within the infobox varies from person to person  Given the large size (700K examples) and heterogeneous nature of the dataset which contains biographies of people from different backgrounds (sports, politics, arts, etc ), it is hard to come up with simple rule-based templates for generating natural language descriptions from infoboxes, thereby making a case for data-driven models  Based on the recent success of data-driven neural models for various other NLG tasks BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , one simple choice is to treat the infobox as a sequence of {field, value} pairs and use a standard seq2seq model for this task  However, such a model is too generic and does not exploit the specific characteristics of this task as explained below  First, note that while generating such descriptions from structured data, a human keeps track of information at two levels  Specifically, at a macro level, she would first decide which field to mention next and then at a micro level decide which of the values in the field needs to be mentioned next  For example, she first decides that at the current step, the field occupation needs attention and then decides which is the next appropriate occupation to attend to from the set of occupations (actor, director, producer, etc )  To enable this, we use a bifocal attention mechanism which computes an attention over fields at a macro level and over values at a micro level  We then fuse these attention weights such that the attention weight for a field also influences the attention over the values within it  Finally, we feed a fused context vector to the decoder which contains both field level and word level information  Note that such two-level attention mechanisms BIBREF6 , BIBREF7 , BIBREF8 have been used in the context of unstructured data (as opposed to structured data in our case), where at a macro level one needs to pay attention to sentences and at a micro level to words in the sentences 
Next, we observe that while rendering the output, once the model pays attention to a field (say, occupation) it needs to stay on this field for a few timesteps (till all the occupations are produced in the output)  We refer to this as the stay on behavior  Further, we note that once the tokens of a field are referred to, they are usually not referred to later  For example, once all the occupations have been listed in the output we will never visit the occupation field again because there is nothing left to say about it  We refer to this as the never look back behavior  To model the stay on behaviour, we introduce a forget (or remember) gate which acts as a signal to decide when to forget the current field (or equivalently to decide till when to remember the current field)  To model the never look back behaviour we introduce a gated orthogonalization mechanism which ensures that once a field is forgotten, subsequent field context vectors fed to the decoder are orthogonal to (or different from) the previous field context vectors 
We experiment with the WikiBio dataset BIBREF0 which contains around 700K {infobox, description} pairs and has a vocabulary of around 400K words  We show that the proposed model gives a relative improvement of 21% and 20% as compared to current state of the art models BIBREF0 , BIBREF9 on this dataset  The proposed model also gives a relative improvement of 10% as compared to the basic seq2seq model  Further, we introduce new datasets for French and German on the same lines as the English WikiBio dataset  Even on these two datasets, our model outperforms the state of the art methods mentioned above 
Related work
Natural Language Generation has always been of interest to the research community and has received a lot of attention in the past  The approaches for NLG range from (i) rule based approaches (e g , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 ) (ii) modular statistical approaches which divide the process into three phases (planning, selection and surface realization) and use data driven approaches for one or more of these phases BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 (iii) hybrid approaches which rely on a combination of handcrafted rules and corpus statistics BIBREF20 , BIBREF21 , BIBREF22 and (iv) the more recent neural network based models BIBREF1  
Neural models for NLG have been proposed in the context of various tasks such as machine translation BIBREF1 , document summarization BIBREF2 , BIBREF4 , paraphrase generation BIBREF23 , image captioning BIBREF24 , video summarization BIBREF25 , query based document summarization BIBREF5 and so on  Most of these models are data hungry and are trained on large amounts of data  On the other hand, NLG from structured data has largely been studied in the context of small datasets such as WeatherGov BIBREF26 , RoboCup BIBREF27 , NFL Recaps BIBREF15 , Prodigy-Meteo BIBREF28 and TUNA Challenge BIBREF29   Recently weather16 proposed RNN/LSTM based neural encoder-decoder models with attention for WeatherGov and RoboCup datasets 
Unlike the datasets mentioned above, the biography dataset introduced by lebret2016neural is larger (700K {table, descriptions} pairs) and has a much larger vocabulary (400K words as opposed to around 350 or fewer words in the above datasets)  Further, unlike the feed-forward neural network based model proposed by BIBREF0 we use a sequence to sequence model and introduce components to address the peculiar characteristics of the task  Specifically, we introduce neural components to address the need for attention at two levels and to address the stay on and never look back behaviour required by the decoder  KiddonZC16 have explored the use of checklists to track previously visited ingredients while generating recipes from ingredients  Note that two-level attention mechanisms have also been used in the context of summarization BIBREF6 , document classification BIBREF7 , dialog systems BIBREF8 , etc  However, these works deal with unstructured data (sentences at the higher level and words at a lower level) as opposed to structured data in our case 
Proposed model
As input we are given an infobox INLINEFORM0 , which is a set of pairs INLINEFORM1 where INLINEFORM2 corresponds to field names and INLINEFORM3 is the sequence of corresponding values and INLINEFORM4 is the total number of fields in INLINEFORM5   For example, INLINEFORM6 could be one such pair in this set  Given such an input, the task is to generate a description INLINEFORM7 containing INLINEFORM8 words  A simple solution is to treat the infobox as a sequence of fields followed by the values corresponding to the field in the order of their appearance in the infobox  For example, the infobox could be flattened to produce the following input sequence (the words in bold are field names which act as delimiters)
[Name] John Doe [Birth_Date] 19 March 1981 [Nationality] Indian      
The problem can then be cast as a seq2seq generation problem and can be modeled using a standard neural architecture comprising of three components (i) an input encoder (using GRU/LSTM cells), (ii) an attention mechanism to attend to important values in the input sequence at each time step and (iii) a decoder to decode the output one word at a time (again, using GRU/LSTM cells)  However, this standard model is too generic and does not exploit the specific characteristics of this task  We propose additional components, viz , (i) a fused bifocal attention mechanism which operates on fields (macro) and values (micro) and (ii) a gated orthogonalization mechanism to model stay on and never look back behavior 
Fused Bifocal Attention Mechanism
Intuitively, when a human writes a description from a table she keeps track of information at two levels  At the macro level, it is important to decide which is the appropriate field to attend to next and at a micro level (i e , within a field) it is important to know which values to attend to next  To capture this behavior, we use a bifocal attention mechanism as described below 
Macro Attention: Consider the INLINEFORM0 -th field INLINEFORM1 which has values INLINEFORM2   Let INLINEFORM3 be the representation of this field in the infobox  This representation can either be (i) the word embedding of the field name or (ii) some function INLINEFORM4 of the values in the field or (iii) a concatenation of (i) and (ii)  The function INLINEFORM5 could simply be the sum or average of the embeddings of the values in the field  Alternately, this function could be a GRU (or LSTM) which treats these values within a field as a sequence and computes the field representation as the final representation of this sequence (i e , the representation of the last time-step)  We found that bidirectional GRU is a better choice for INLINEFORM6 and concatenating the embedding of the field name with this GRU representation works best  Further, using a bidirectional GRU cell to take contextual information from neighboring fields also helps (these are the orange colored cells in the top-left block in Figure FIGREF3 with macro attention)  Given these representations INLINEFORM7 for all the INLINEFORM8 fields we compute an attention over the fields (macro level)  DISPLAYFORM0
where INLINEFORM0 is the state of the decoder at time step INLINEFORM1   INLINEFORM2 and INLINEFORM3 are parameters, INLINEFORM4 is the total number of fields in the input, INLINEFORM5 is the macro (field level) context vector at the INLINEFORM6 -th time step of the decoder 
Micro Attention: Let INLINEFORM0 be the representation of the INLINEFORM1 -th value in a given field  This representation could again either be (i) simply the embedding of this value (ii) or a contextual representation computed using a function INLINEFORM2 which also considers the other values in the field  For example, if INLINEFORM3 are the values in a field then these values can be treated as a sequence and the representation of the INLINEFORM4 -th value can be computed using a bidirectional GRU over this sequence  Once again, we found that using a bi-GRU works better then simply using the embedding of the value  Once we have such a representation computed for all values across all the fields, we compute the attention over these values (micro level) as shown below : DISPLAYFORM0
where INLINEFORM0 is the state of the decoder at time step INLINEFORM1   INLINEFORM2 and INLINEFORM3 are parameters, INLINEFORM4 is the total number of values across all the fields 
Fused Attention: Intuitively, the attention weights assigned to a field should have an influence on all the values belonging to the particular field  To ensure this, we reweigh the micro level attention weights based on the corresponding macro level attention weights  In other words, we fuse the attention weights at the two levels as: DISPLAYFORM0
where INLINEFORM0 is the field corresponding to the INLINEFORM1 -th value, INLINEFORM2 is the macro level context vector 
Gated Orthogonalization for Modeling Stay-On and Never Look Back behaviour
We now describe a series of choices made to model stay-on and never look back behavior  We first begin with the stay-on property which essentially implies that if we have paid attention to the field INLINEFORM0 at timestep INLINEFORM1 then we are likely to pay attention to the same field for a few more time steps  For example, if we are focusing on the occupation field at this timestep then we are likely to focus on it for the next few timesteps till all relevant values in this field have been included in the generated description  In other words, we want to remember the field context vector INLINEFORM2 for a few timesteps  One way of ensuring this is to use a remember (or forget) gate as given below which remembers the previous context vector when required and forgets it when it is time to move on from that field  DISPLAYFORM0
where INLINEFORM0 are parameters to be learned  The job of the forget gate is to ensure that INLINEFORM1 is similar to INLINEFORM2 when required (i e , by learning INLINEFORM3 when we want to continue focusing on the same field) and different when it is time to move on (by learning that INLINEFORM4 ) 
Next, the never look back property implies that once we have moved away from a field we are unlikely to pay attention to it again  For example, once we have rendered all the occupations in the generated description there is no need to return back to the occupation field  In other words, once we have moved on ( INLINEFORM0 ), we want the successive field context vectors INLINEFORM1 to be very different from the previous field vectors INLINEFORM2   One way of ensuring this is to orthogonalize successive field vectors using DISPLAYFORM0
where INLINEFORM0 is the dot product between vectors INLINEFORM1 and INLINEFORM2   The above equation essentially subtracts the component of INLINEFORM3 along INLINEFORM4   INLINEFORM5 is a learned parameter which controls the degree of orthogonalization thereby allowing a soft orthogonalization (i e , the entire component along INLINEFORM6 is not subtracted but only a fraction of it)  The above equation only ensures that INLINEFORM7 is soft-orthogonal to INLINEFORM8   Alternately, we could pass the sequence of context vectors, INLINEFORM9 generated so far through a GRU cell  The state of this GRU cell at each time step would thus be aware of the history of the field vectors till that timestep  Now instead of orthogonalizing INLINEFORM10 to INLINEFORM11 we could orthogonalize INLINEFORM12 to the hidden state of this GRU at time-step INLINEFORM13   In practice, we found this to work better as it accounts for all the field vectors in the history instead of only the previous field vector 
In summary, Equation provides a mechanism for remembering the current field vector when appropriate (thus capturing stay-on behavior) using a remember gate  On the other hand, Equation EQREF10 explicitly ensures that the field vector is very different (soft-orthogonal) from the previous field vectors once it is time to move on (thus capturing never look back behavior)  The value of INLINEFORM0 computed in Equation EQREF10 is then used in Equation   The INLINEFORM1 (macro) thus obtained is then concatenated with INLINEFORM2 (micro) and fed to the decoder (see Fig  FIGREF3 )
Experimental setup
We now describe our experimental setup:
Datasets
We use the WikiBio dataset introduced by lebret2016neural  It consists of INLINEFORM0 biography articles from English Wikipedia  A biography article corresponds to a person (sportsman, politician, historical figure, actor, etc )  Each Wikipedia article has an accompanying infobox which serves as the structured input and the task is to generate the first sentence of the article (which typically is a one-line description of the person)  We used the same train, valid and test sets which were made publicly available by lebret2016neural 
We also introduce two new biography datasets, one in French and one in German  These datasets were created and pre-processed using the same procedure as outlined in lebret2016neural  Specifically, we extracted the infoboxes and the first sentence from the corresponding Wikipedia article  As with the English dataset, we split the French and German datasets randomly into train (80%), test (10%) and valid (10%)  The French and German datasets extracted by us has been made publicly available  The number of examples was 170K and 50K and the vocabulary size was 297K and 143K for French and German respectively  Although in this work we focus only on generating descriptions in one language, we hope that this dataset will also be useful for developing models which jointly learn to generate descriptions from structured data in multiple languages 
Models compared
We compare with the following models:
1  BIBREF0 : This is a conditional language model which uses a feed-forward neural network to predict the next word in the description conditioned on local characteristics (i e , words within a field) and global characteristics (i e , overall structure of the infobox) 
2  BIBREF9 : This model was proposed in the context of the WeatherGov and RoboCup datasets which have a much smaller vocabulary  They use an improved attention model with additional regularizer terms which influence the weights assigned to the fields 
3  Basic Seq2Seq: This is the vanilla encode-attend-decode model BIBREF1   Further, to deal with the large vocabulary ( INLINEFORM0 400K words) we use a copying mechanism as a post-processing step  Specifically, we identify the time steps at which the decoder produces unknown words (denoted by the special symbol UNK)  For each such time step, we look at the attention weights on the input words and replace the UNK word by that input word which has received maximum attention at this timestep  This process is similar to the one described in BIBREF30   Even lebret2016neural have a copying mechanism tightly integrated with their model 
Hyperparameter tuning
We tuned the hyperparameters of all the models using a validation set  As mentioned earlier, we used a bidirectional GRU cell as the function INLINEFORM0 for computing the representation of the fields and the values (see Section SECREF4 )  For all the models, we experimented with GRU state sizes of 128, 256 and 512  The total number of unique words in the corpus is around 400K (this includes the words in the infobox and the descriptions)  Of these, we retained only the top 20K words in our vocabulary (same as BIBREF0 )  We initialized the embeddings of these words with 300 dimensional Glove embeddings BIBREF31   We used Adam BIBREF32 with a learning rate of INLINEFORM1 , INLINEFORM2 and INLINEFORM3   We trained the model for a maximum of 20 epochs and used early stopping with the patience set to 5 epochs 
Results and Discussions
We now discuss the results of our experiments 
Comparison of different models
Following lebret2016neural, we used BLEU-4, NIST-4 and ROUGE-4 as the evaluation metrics  We first make a few observations based on the results on the English dataset (Table TABREF15 )  The basic seq2seq model, as well as the model proposed by weather16, perform better than the model proposed by lebret2016neural  Our final model with bifocal attention and gated orthogonalization gives the best performance and does 10% (relative) better than the closest baseline (basic seq2seq) and 21% (relative) better than the current state of the art method BIBREF0   In Table TABREF16 , we show some qualitative examples of the output generated by different models 
Human Evaluations
To make a qualitative assessment of the generated sentences, we conducted a human study on a sample of 500 Infoboxes which were sampled from English dataset  The annotators for this task were undergraduate and graduate students  For each of these infoboxes, we generated summaries using the basic seq2seq model and our final model with bifocal attention and gated orthogonalization  For each description and for each model, we asked three annotators to rank the output of the systems based on i) adequacy (i e  does it capture relevant information from the infobox), (ii) fluency (i e  grammar) and (iii) relative preference (i e , which of the two outputs would be preferred)  Overall the average fluency/adequacy (on a scale of 5) for basic seq2seq model was INLINEFORM0 and INLINEFORM1 for our model respectively 
The results from Table TABREF17 suggest that in general gated orthogonalization model performs better than the basic seq2seq model  Additionally, annotators were asked to verify if the generated summaries look natural (i e, as if they were generated by humans)  In 423 out of 500 cases, the annotators said “Yes” suggesting that gated orthogonalization model indeed produces good descriptions 
Performance on different languages
The results on the French and German datasets are summarized in Tables TABREF20 and TABREF20 respectively  Note that the code of BIBREF0 is not publicly available, hence we could not report numbers for French and German using their model  We observe that our final model gives the best performance - though the bifocal attention model performs poorly as compared to the basic seq2seq model on French  However, the overall performance for French and German are much smaller than those for English  There could be multiple reasons for this  First, the amount of training data in these two languages is smaller than that in English  Specifically, the amount of training data available in French (German) is only INLINEFORM0 ( INLINEFORM1 )% of that available for English  Second, on average the descriptions in French and German are longer than that in English (EN: INLINEFORM2 words, FR: INLINEFORM3 words and DE: INLINEFORM4 words)  Finally, a manual inspection across the three languages suggests that the English descriptions have a more consistent structure than the French descriptions  For example, most English descriptions start with name followed by date of birth but this is not the case in French  However, this is only a qualitative observation and it is hard to quantify this characteristic of the French and German datasets 
Visualizing Attention Weights
If the proposed model indeed works well then we should see attention weights that are consistent with the stay on and never look back behavior  To verify this, we plotted the attention weights in cases where the model with gated orthogonalization does better than the model with only bifocal attention  Figure FIGREF21 shows the attention weights corresponding to infobox in Figure FIGREF25   Notice that the model without gated orthogonalization has attention on both name field and article title while rendering the name  The model with gated orthogonalization, on the other hand, stays on the name field for as long as it is required but then moves and never returns to it (as expected) 
Due to lack of space, we do not show similar plots for French and German but we would like to mention that, in general, the differences between the attention weights learned by the model with and without gated orthogonalization were more pronounced for the French/German dataset than the English dataset  This is in agreement with the results reported in Table TABREF20 and TABREF20 where the improvements given by gated orthogonalization are more for French/German than for English 
Out of domain results
What if the model sees a different INLINEFORM0 of person at test time? For example, what if the training data does not contain any sportspersons but at test time we encounter the infobox of a sportsperson  This is the same as seeing out-of-domain data at test time  Such a situation is quite expected in the products domain where new products with new features (fields) get frequently added to the catalog  We were interested in three questions here  First, we wanted to see if testing the model on out-of-domain data indeed leads to a drop in the performance  For this, we compared the performance of our best model in two scenarios (i) trained on data from all domains (including the target domain) and tested on the target domain (sports, arts) and (ii) trained on data from all domains except the target domain and tested on the target domain  Comparing rows 1 and 2 of Table TABREF32 we observed a significant drop in the performance  Note that the numbers for sports domain in row 1 are much better than the Arts domain because roughly 40% of the WikiBio training data contains sportspersons 
Next, we wanted to see if we can use a small amount of data from the target domain to fine tune a model trained on the out of domain data  We observe that even with very small amounts of target domain data the performance starts improving significantly (see rows 3 and 4 of Table TABREF32 )  Note that if we train a model from scratch with only limited data from the target domain instead of fine-tuning a model trained on a different source domain then the performance is very poor  In particular, training a model from scratch with 10K training instances we get a BLEU score of INLINEFORM0 and INLINEFORM1 for arts and sports respectively  Finally, even though the actual words used for describing a sportsperson (footballer, cricketer, etc ) would be very different from the words used to describe an artist (actor, musician, etc ) they might share many fields (for example, date of birth, occupation, etc )  As seen in Figure FIGREF28 (attention weights corresponding to the infobox in Figure FIGREF27 ), the model predicts the attention weights correctly for common fields (such as occupation) but it is unable to use the right vocabulary to describe the occupation (since it has not seen such words frequently in the training data)  However, once we fine tune the model with limited data from the target domain we see that it picks up the new vocabulary and produces a correct description of the occupation 
Conclusion
We present a model for generating natural language descriptions from structured data  To address specific characteristics of the problem we propose neural components for fused bifocal attention and gated orthogonalization to address stay on and never look back behavior while decoding  Our final model outperforms an existing state of the art model on a large scale WikiBio dataset by 21%  We also introduce datasets for French and German and demonstrate that our model gives state of the art results on these datasets  Finally, we perform experiments with an out-of-domain model and show that if such a model is fine-tuned with small amounts of in domain data then it can give an improved performance on the target domain 
Given the multilingual nature of the new datasets, as future work, we would like to build models which can jointly learn to generate natural language descriptions from structured data in multiple languages  One idea is to replace the concepts in the input infobox by Wikidata concept ids which are language agnostic  A large amount of input vocabulary could thus be shared across languages thereby facilitating joint learning 
Acknowledgements
We thank Google for supporting Preksha Nema through their Google India Ph D  Fellowship program  We also thank Microsoft Research India for supporting Shreyas Shetty through their generous travel grant for attending the conference 

ground_truth: BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800

BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800

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. Do they use pretrained embeddings?
Context 3: 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

ground_truth: Yes

Yes

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 3: 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 

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