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. Are the images from a specific domain?
Context 1: Introduction
Automatically generating text to describe the content of images, also known as image captioning, is a multimodal task of considerable interest in both the computer vision and the NLP communities  Image captioning can be framed as a translation task from an image to a descriptive natural language statement  Many existing captioning models BIBREF0, BIBREF1, BIBREF2, BIBREF3 follow the typical encoder-decoder framework where a convolutional network is used to condense images into visual feature representations, combined with a recurrent network for language generation  While these models demonstrate promising results, quantifying image captioning performance remains a challenging problem, in a similar way to other generative tasks BIBREF4, BIBREF5 
Evaluating candidate captions for human preference is slow and laborious  To alleviate this problem, many automatic evaluation metrics have been proposed, such as BLEU BIBREF6, METEOR BIBREF7, ROUGE BIBREF8 and CIDEr BIBREF9  These n-gram-based metrics evaluate captioning performance based on surface similarity between a candidate caption and reference statements  A more recent evaluation metric for image captioning is SPICE BIBREF10, which takes into account semantic propositional content of generated captions by scoring a caption based upon a graph-based semantic representation transformed from reference captions 
The rationale behind these evaluation metrics is that human reference captions serve as an approximate target and comparing model outputs to this target is a proxy for how well a system performs  Thus, a candidate caption is not directly evaluated with respect to image content, but compared to a set of human statements about that image 
However, in image captioning, visual scenes with multiple objects and relations correspond to a diversity of valid descriptions  Consider the example image and captions from the ShapeWorld framework BIBREF11 shown in Figure FIGREF1  The first three captions are true statements about the image and express relevant ideas, but describe different objects, attributes and spatial relationships, while the fourth caption is wrong despite referring to the same objects as in the third caption  This casts doubt on the sufficiency of using a set of reference captions to approximate the content of an image  We argue that, while existing metrics have undeniably been useful for real-world captioning evaluation, their focus on approximate surface comparison limits deeper insights into the learning process and eventual behavior of captioning models 
To address this problem, we propose a set of principled evaluation criteria which evaluate image captioning models for grammaticality, truthfulness and diversity (GTD)  These criteria correspond to necessary requirements for image captioning systems: (a) that the output is grammatical, (b) that the output statement is true with respect to the image, and (c) that outputs are diverse and mirror the variability of training captions 
Practical evaluation of GTD is currently only possible on synthetic data  We construct a range of datasets designed for image captioning evaluation  We call this diagnostic evaluation benchmark ShapeWorldICE (ShapeWorld for Image Captioning Evaluation)  We illustrate the evaluation of specific image captioning models on ShapeWorldICE  We empirically demonstrate that the existing metrics BLEU and SPICE do not capture true caption-image agreement in all scenarios, while the GTD framework allows a fine-grained investigation of how well existing models cope with varied visual situations and linguistic constructions 
We believe that as a supplementary evaluation method to real-world metrics, the GTD framework provides evaluation insights that are sufficiently interesting to motivate future work 
Related work ::: Existing evaluation of image captioning
As a natural language generation task, image captioning frequently uses evaluation metrics such as BLEU BIBREF6, METEOR BIBREF7, ROUGE BIBREF8 and CIDEr BIBREF9  These metrics use n-gram similarity between the candidate caption and reference captions to approximate the correlation between a candidate caption and the associated ground truth  SPICE BIBREF10 is a more recent metric specifically designed for image captioning  For SPICE, both the candidate caption and reference captions are parsed to scene graphs, and the agreement between tuples extracted from these scene graphs is examined  SPICE more closely relates to our truthfulness evaluation than the other metrics, but it still uses overlap comparison to reference captions as a proxy to ground truth  In contrast, our truthfulness metric directly evaluates a candidate caption against a model of the actual visual content 
Many researchers have pointed out problems with existing reference-based metrics including low correlations with human judgment BIBREF12, BIBREF10, BIBREF13 and strong baselines using nearest-neighbor methods BIBREF14 or relying solely on object detection BIBREF15  Fundamental concerns have been raised with respect to BLEU, including variability in parameterization and precise score calculation leading to significantly different results BIBREF16  Its validity as a metric for tasks other than machine translation has been questioned BIBREF17, particularly for tasks for which the output content is not narrowly constrained, like dialogue BIBREF18 
Some recent work focuses on increasing the diversity of generated captions, for which various measures are proposed  Devlin et al  BIBREF19 explored the concept of caption diversity by evaluating performance on compositionally novel images  van Miltenburg et al BIBREF20 framed image captioning as a word recall task and proposed several metrics, predominantly focusing on diversity at the word level  However, this direction is still relatively new and lacks standardized benchmarks and metrics 
Related work ::: Synthetic datasets
Recently, many synthetic datasets have been proposed as diagnostic tools for deep learning models, such as CLEVR BIBREF21 for visual question answering (VQA), the bAbI tasks BIBREF22 for text understanding and reasoning, and ShapeWorld BIBREF11 for visually grounded language understanding  The primary motivation is to reduce complexity which is considered irrelevant to the evaluation focus, to enable better control over the data, and to provide more detailed insights into strengths and limitations of existing models 
In this work, we develop the evaluation datasets within the ShapeWorld framework  ShapeWorld is a controlled data generation framework consisting of abstract colored shapes (see Figure FIGREF1 for an example)  We use ShapeWorld to generate training and evaluation data for two major reasons  ShapeWorld supports customized data generation according to user specification, which enables a variety of model inspections in terms of language construction, visual complexity and reasoning ability  Another benefit is that each training and test instance generated in ShapeWorld is returned as a triplet of $<$image, caption, world model$>$  The world model stores information about the underlying microworld used to generate an image and a descriptive caption, internally represented as a list of entities with their attributes, such as shape, color, position  During data generation, ShapeWorld randomly samples a world model from a set of available entities and attributes  The generated world model is then used to realize a corresponding instance consisting of image and caption  The world model gives the actual semantic information contained in an image, which allows evaluation of caption truthfulness 
GTD Evaluation Framework
In the following we introduce GTD in more detail, consider it as an evaluation protocol covering necessary aspects of the multifaceted captioning task, rather than a specific metric 
GTD Evaluation Framework ::: Grammaticality
An essential criterion for an image captioning model is that the captions generated are grammatically well-formed  Fully accurate assessment of grammaticality in a general context is itself a difficult task, but becomes more feasible in a very constrained context like our diagnostic language data  We take parseability with the English Resource Grammar BIBREF23 as a surrogate for grammaticality, meaning that a sentence is considered grammatically well-formed if we obtain a parse using the ERG 
The ERG is a broad-coverage grammar based on the head-driven phrase structure grammar (HPSG) framework  It is linguistically precise: sentences only parse if they are valid according to its hand-built rules  It is designed to be general-purpose: verified coverage is around 80% for Wikipedia, and over 90% for corpora with shorter sentences and more limited vocabulary (for details see BIBREF24 flickinger2011accuracy)  Since the ShapeWorld training data – the only language source for models to learn from – is generated using the same grammar, the ERG has $\sim $100% coverage of grammaticality in the model output space 
GTD Evaluation Framework ::: Truthfulness
The second aspect we investigate is truthfulness, that is, whether a candidate caption is compatible with the content of the image it is supposed to describe  We evaluate caption truthfulness on the basis of a linguistically-motivated approach using formal semantics  We convert the output of the ERG parse for a grammatical caption to a Dependency Minimal Recursion Semantics (DMRS) graph using the pydmrs tool BIBREF25  Each converted DMRS is a logical semantic graph representation corresponding to the caption  We construct a logical proposition from the DMRS graph, and evaluate it against the actual world model of the corresponding image  A caption can be said to agree with an image only if the proposition evaluates as true on the basis of the world model  By examining the logical agreement between a caption representation and a world model, we can check whether the semantics of this caption agrees with the visual content which the world model represents  Thus we do not rely on a set of captions as a surrogate for the content of an image, but instead leverage the fact that we have the ground truth, thus enabling the evaluation of true image-caption agreement 
GTD Evaluation Framework ::: Diversity
While grammaticality and truthfulness are essential requirements for image captions, these criteria alone can easily be “gamed” by specializing on a small set of generic statements which are true most of the time  In the context of abstract shapes, such captions include examples like “There is a shape” or “At least zero shapes are blue” (which is technically true even if there is no blue shape)  This motivates the third fundamental requirement of captioning output to be diverse 
As ShapeWorldICE exploits a limited size of open-class words, we emphasize the diversity in ShapeWorldICE at the sentence level rather than the word level  Since the ground-truth reference captions in ShapeWorld are randomly sampled, we take the sampled captions accompanying the test images as a proxy for optimal caption diversity, and compare it with the empirical output diversity of the evaluated model on these test images  Practically, we look at language constructions used and compute the corresponding diversity score as the ratio of observed number versus optimal number:
Language constructions here correspond to reduced caption representations which only record whether an object is described by shape (e g , “square”), color (e g , “red shape”) or color-shape combination (e g , “red square”)  So the statement “A square is red” and “A circle is blue” are considered the same, while “A shape is red” is different 
Experimental Setup ::: Datasets
We develop a variety of ShapeWorldICE datasets, with a similar idea to the “skill tasks” in the bAbI framework BIBREF22  Table TABREF4 gives an overview for different ShapeWorldICE datasets we use in this paper  We consider three different types of captioning tasks, each of which focuses on a distinct aspect of reasoning abilities  Existential descriptions examine whether a certain object is present in an image  Spatial descriptions identify spatial relationships among visual objects  Quantification descriptions involve count-based and ratio-based statements, with an explicit focus on inspecting models for their counting ability  We develop two variants for each type of dataset to enable different levels of visual complexity or specific aspects of the same reasoning type  All the training and test captions sampled in this work are in English 
Each dataset variant consists of around 200k training instances and 4,096 validation instances, plus 4,096 test instances  Each training instance consists of an image and a reference caption  At test time, only the test images are available to the evaluated models  Underlying world models are kept from the models and are used for later GTD evaluation  For each test instance, we sample ten reference captions of the same distribution as the training captions to enable the comparison of our proposed metrics to BLEU and SPICE  We fine-tune our model hyperparameters based on the performance on the validation set  All reported results are measured on the test split with the parameters yielding the best validation performance 
Experimental Setup ::: Models
We experiment with two image captioning models: the Show&Tell model BIBREF0 and the LRCN1u model BIBREF1  Both models follow the basic encoder-decoder architecture design that uses a CNN encoder to condense the visual information into an image embedding, which in turn conditions an LSTM decoder to generate a natural language caption  The main difference between the two models is the way they condition the decoder  The Show&Tell model feeds the image embedding as the “predecessor word embedding” to the first produced word, while the LRCN1u model concatenates the image features with the embedded previous word as the input to the sequence model at each time step 
We follow the common practice in image captioning to use a CNN component pretrained on object detection and fine-tune its parameters on the image captioning task  The encoder and decoder components are jointly optimized with respect to the standard cross-entropy sequence loss on the respective ShapeWorldICE dataset  For all our experiments, we train models end-to-end for a fixed number of 100k iterations with a batch size of 64  We use Adam optimization BIBREF26 with a learning rate of 0 001  Word embeddings are randomly initialized and jointly trained during the training 
Results
We train and evaluate the Show&Tell and LRCN1u models on the ShapeWorldICE datasets  Here we discuss in detail the diagnostic results of these experiments  During training, we periodically record model output on the test images, to be able to analyze the development of our evaluation metrics throughout the process  We also compute BLEU-4 scores and SPICE scores of generated captions for comparison, using 10 reference captions per test image 
LRCN1u exhibits clearly superior performance in terms of truthfulness  We start off by comparing performance of the Show&Tell model and the LRCN1u model, see Figure FIGREF8  While both models learn to produce grammatical sentences early on, it can be seen that LRCN1u is clearly superior in terms of truthfulness, achieving 100% halfway through training, whereas Show&Tell only slowly reaches around 90% by the end of 100k iterations  This indicates that incorporating visual features at every generation step is beneficial for producing true captions  The diversity ratios of captions generated by two models both increase substantially as the training progresses, with LRCN1u exhibiting a slightly greater caption diversity at the end of training 
We observed similar results on other ShapeWorldICE datasets that we experimented with, validating the superiority of LRCN1u over Show&Tell on ShapeWorldICE  Consequently, we decided to focus on the LRCN1u architecture in subsequent evaluations, where we report detailed results with respect to the GTD framework on a variety of datasets 
Correlation between the BLEU/SPICE scores and the ground truth  From the learning curves shown in Figure FIGREF9, we find low or no correlation between the BLEU/SPICE scores and caption truthfulness 
On Existential-OneShape, the BLEU curve follows the trend of the truthfulness curve in general, indicating that BLEU is able to capture caption truthfulness well in this simple scenario  However, while BLEU reports equivalent model performance on Existential-MultiShapes and Spatial-MultiShapes, the truthfulness metric demonstrates very different results  The BLEU score for generated Existential-MultiShapes captions increases rapidly at the beginning of training and then plateaus despite the continuous increase in truthfulness ratio  Captions generated on Spatial-MultiShapes attain a relatively high BLEU score from an early stage of training, but exhibit low agreement ($<$0 6 truthfulness ratio) with ground-truth visual scenes  In the case of Spatial-MultiShapes, spatial descriptors for two objects are chosen from a fixed set (“above”, “below”, “to the left of” and “to the right of”)  It is very likely for a generated spatial descriptor to match one of the descriptors mentioned in reference captions  In this particular case, the model is apt to infer a caption which has high n-gram overlaps with reference captions, resulting in a relatively high BLEU score  Thus an increased BLEU score does not necessarily indicate improved performance 
While the truthfulness and BLEU scores in Figure FIGREF9 both increase rapidly early on and then stay stable at a high rate after training for 20k iterations, the SPICE curve instead shows a downward trend in the later stage of training  We examined the output SPICE score for each test instance  SPICE reports a precision score of 1 0 for most test instances after 20k iterations, which is consistent with the truthfulness and BLEU scores  However, SPICE forms the reference scene graph as the union of the scene graphs extracted from individual reference captions, thus introducing redundancies  SPICE uses the F1 score of scene graph matching between the candidate and reference and hence is lowered by imperfect recall 
Comparing SPICE curves for three datasets shown in Figure FIGREF9-FIGREF9, they suggest an increase in task complexity, but they do not reflect the successively closing gap of caption truthfulness scores between two Existential datasets, or the substantial difference in caption truthfulness between captions on Existential-MultiShapes and Spatial-MultiShapes 
In the remainder of the paper we discuss in detail the diagnostic results of the LRCN1u model demonstrated by the GTD evaluation framework 
Perfect grammaticality for all caption types  As shown in Figure FIGREF15, generated captions for all types of ShapeWorldICE datasets attain quasi-perfect grammaticality scores in fewer than 5,000 iterations, suggesting that the model quickly learns to generate grammatically well-formed sentences 
Failure to learn complex spatial relationships  While CNNs can produce rich visual representations that can be used for a variety of vision tasks BIBREF27, it remains an open question whether these condensed visual representations are rich enough for multimodal tasks that require higher-level abilities of scene understanding and visual reasoning  From Figure FIGREF16, we can see that while the model performs rather well on Existential datasets, it exhibits a worse performance on Spatial data  The caption agreement ratio in the simple Spatial-TwoShapes scenario is relatively high, but drops significantly on Spatial-MultiShapes, demonstrating the deficiencies of the model in learning spatial relationships from complex visual scenes 
The counting task is non-trivial  Counting has long been considered to be a challenging task in multimodal reasoning BIBREF28, BIBREF29  To explore how well the LRCN1u model copes with counting tasks, we generated two Quantification datasets  The Quant-Count captions describe the number of objects with certain attributes that appear in an image (e g  “Exactly four shapes are crosses”), while the Quant-Ratio captions describe the ratio of certain objects (e g  “A third of the shapes are blue squares”) 
From Figure FIGREF16, we notice that the LRCN1u model performs poorly on these datasets in terms of truthfulness, reflected in the 0 50 and 0 46 scores achieved by the model on the Quant-Count and Quant-Ratio tasks respectively  The learning curve for Quant-Ratio exhibits a more gradual rise as the training progresses, suggesting a greater complexity for the ratio-based task 
Caption diversity benefits from varied language constructions in the training data  The diversity ratios of generated captions for different ShapeWorldICE datasets are illustrated in Figure FIGREF17  We can see that the diversity of inferred captions is largely sensitive to the caption variability in the dataset itself  For simple datasets (such as Existential-OneShape) where language constructions in the training set are less diverse, the output captions tend to have uniform sentence structures  The high diversity ratios of generated Spatial and Quantification captions suggest that caption diversity benefits from heterogeneous language constructions in complex datasets 
Discussions and Conclusions
Evaluation metrics are required as a proxy for performance in real applications  As such, they should, as far as possible, allow measurement of fundamental aspects of the performance of models on tasks  In this work, we propose the GTD evaluation framework as a supplement to standard image captioning evaluation which explicitly focuses on grammaticality, truthfulness and diversity  We developed the ShapeWorldICE evaluation suite to allow in-depth and fine-grained inspection of model behaviors  We have empirically verified that GTD captures different aspects of performance to existing metrics by evaluating image captioning models on the ShapeWorldICE suite  We hope that this framework will shed light on important aspects of model behaviour and that this will help guide future research efforts 
While performing the evaluation experiments on the LRCN1u model, we noticed that caption agreement does not always improve as the training loss decreases  Ideally, the training objective should be in accordance with how a model is eventually evaluated  In future work, we plan to investigate the feasibility of deliberately encoding the GTD signal in the training process, for instance, by implementing a GTD-aware loss  We also plan to extend the existing ShapeWorldICE benchmark to include more linguistic constructions (such as relative clauses, compound sentences and coreference)  By doing so, we hope to reveal how well existing image captioning models cope with complex generation tasks 
Acknowledgments
We thank the anonymous reviewers for their constructive feedback  HX is grateful for being supported by the CSC Cambridge Scholarship  TS is supported in part by the EPSRC Centre for Doctoral Training in Data Science, funded by the EPSRC (grant EP/L016427/1) and the University of Edinburgh  AK is grateful for being supported by a Qualcomm Research Studentship and an EPSRC Doctoral Training Studentship 
Context 2: Introduction
Word Sense Disambiguation (WSD) is a fundamental task and long-standing challenge in Natural Language Processing (NLP), which aims to find the exact sense of an ambiguous word in a particular context BIBREF0  Previous WSD approaches can be grouped into two main categories: knowledge-based and supervised methods 
Knowledge-based WSD methods rely on lexical resources like WordNet BIBREF1 and usually exploit two kinds of lexical knowledge  The gloss, which defines a word sense meaning, is first utilized in Lesk algorithm BIBREF2 and then widely taken into account in many other approaches BIBREF3, BIBREF4  Besides, structural properties of semantic graphs are mainly used in graph-based algorithms BIBREF5, BIBREF6 
Traditional supervised WSD methods BIBREF7, BIBREF8, BIBREF9 focus on extracting manually designed features and then train a dedicated classifier (word expert) for every target lemma 
Although word expert supervised WSD methods perform better, they are less flexible than knowledge-based methods in the all-words WSD task BIBREF10  Recent neural-based methods are devoted to dealing with this problem  BIBREF11 present a supervised classifier based on Bi-LSTM, which shares parameters among all word types except the last layer  BIBREF10 convert WSD task to a sequence labeling task, thus building a unified model for all polysemous words  However, neither of them can totally beat the best word expert supervised methods 
More recently, BIBREF12 propose to leverage the gloss information from WordNet and model the semantic relationship between the context and gloss in an improved memory network  Similarly, BIBREF13 introduce a (hierarchical) co-attention mechanism to generate co-dependent representations for the context and gloss  Their attempts prove that incorporating gloss knowledge into supervised WSD approach is helpful, but they still have not achieved much improvement, because they may not make full use of gloss knowledge 
In this paper, we focus on how to better leverage gloss information in a supervised neural WSD system  Recently, the pre-trained language models, such as ELMo BIBREF14 and BERT BIBREF15, have shown their effectiveness to alleviate the effort of feature engineering  Especially, BERT has achieved excellent results in question answering (QA) and natural language inference (NLI)  We construct context-gloss pairs from glosses of all possible senses (in WordNet) of the target word, thus treating WSD task as a sentence-pair classification problem  We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task  In particular, our contribution is two-fold:
1  We construct context-gloss pairs and propose three BERT-based models for WSD 
2  We fine-tune the pre-trained BERT model, and the experimental results on several English all-words WSD benchmark datasets show that our approach significantly outperforms the state-of-the-art systems 
Methodology
In this section, we describe our method in detail 
Methodology ::: Task Definition
In WSD, a sentence $s$ usually consists of a series of words: $\lbrace w_1,\cdots ,w_m\rbrace $, and some of the words $\lbrace w_{i_1},\cdots ,w_{i_k}\rbrace $ are targets $\lbrace t_1,\cdots ,t_k\rbrace $ need to be disambiguated  For each target $t$, its candidate senses $\lbrace c_1,\cdots ,c_n\rbrace $ come from entries of its lemma in a pre-defined sense inventory (usually WordNet)  Therefore, WSD task aims to find the most suitable entry (symbolized as unique sense key) for each target in a sentence  See a sentence example in Table TABREF1 
Methodology ::: BERT
BERT BIBREF15 is a new language representation model, and its architecture is a multi-layer bidirectional Transformer encoder  BERT model is pre-trained on a large corpus and two novel unsupervised prediction tasks, i e , masked language model and next sentence prediction tasks are used in pre-training  When incorporating BERT into downstream tasks, the fine-tuning procedure is recommended  We fine-tune the pre-trained BERT model on WSD task 
Methodology ::: BERT ::: BERT(Token-CLS)
Since every target in a sentence needs to be disambiguated to find its exact sense, WSD task can be regarded as a token-level classification task  To incorporate BERT to WSD task, we take the final hidden state of the token corresponding to the target word (if more than one token, we average them) and add a classification layer for every target lemma, which is the same as the last layer of the Bi-LSTM model BIBREF11 
Methodology ::: GlossBERT
BERT can explicitly model the relationship of a pair of texts, which has shown to be beneficial to many pair-wise natural language understanding tasks  In order to fully leverage gloss information, we propose GlossBERT to construct context-gloss pairs from all possible senses of the target word in WordNet, thus treating WSD task as a sentence-pair classification problem 
We describe our construction method with an example (See Table TABREF1)  There are four targets in this sentence, and here we take target word research as an example:
Methodology ::: GlossBERT ::: Context-Gloss Pairs
The sentence containing target words is denoted as context sentence  For each target word, we extract glosses of all $N$ possible senses (here $N=4$) of the target word (research) in WordNet to obtain the gloss sentence  [CLS] and [SEP] marks are added to the context-gloss pairs to make it suitable for the input of BERT model  A similar idea is also used in aspect-based sentiment analysis BIBREF16 
Methodology ::: GlossBERT ::: Context-Gloss Pairs with Weak Supervision
Based on the previous construction method, we add weak supervised signals to the context-gloss pairs (see the highlighted part in Table TABREF1)  The signal in the gloss sentence aims to point out the target word, and the signal in the context sentence aims to emphasize the target word considering the situation that a target word may occur more than one time in the same sentence 
Therefore, each target word has $N$ context-gloss pair training instances ($label\in \lbrace yes, no\rbrace $)  When testing, we output the probability of $label=yes$ of each context-gloss pair and choose the sense corresponding to the highest probability as the prediction label of the target word  We experiment with three GlossBERT models:
Methodology ::: GlossBERT ::: GlossBERT(Token-CLS)
We use context-gloss pairs as input  We highlight the target word by taking the final hidden state of the token corresponding to the target word (if more than one token, we average them) and add a classification layer ($label\in \lbrace yes, no\rbrace $) 
Methodology ::: GlossBERT ::: GlossBERT(Sent-CLS)
We use context-gloss pairs as input  We take the final hidden state of the first token [CLS] as the representation of the whole sequence and add a classification layer ($label\in \lbrace yes, no\rbrace $), which does not highlight the target word 
Methodology ::: GlossBERT ::: GlossBERT(Sent-CLS-WS)
We use context-gloss pairs with weak supervision as input  We take the final hidden state of the first token [CLS] and add a classification layer ($label\in \lbrace yes, no\rbrace $), which weekly highlight the target word by the weak supervision 
Experiments ::: Datasets
The statistics of the WSD datasets are shown in Table TABREF12 
Experiments ::: Datasets ::: Training Dataset
Following previous work BIBREF13, BIBREF12, BIBREF10, BIBREF17, BIBREF9, BIBREF7, we choose SemCor3 0 as training corpus, which is the largest corpus manually annotated with WordNet sense for WSD 
Experiments ::: Datasets ::: Evaluation Datasets
We evaluate our method on several English all-words WSD datasets  For a fair comparison, we use the benchmark datasets proposed by BIBREF17 which include five standard all-words fine-grained WSD datasets from the Senseval and SemEval competitions: Senseval-2 (SE2), Senseval-3 (SE3), SemEval-2007 (SE07), SemEval-2013 (SE13) and SemEval-2015 (SE15)  Following BIBREF13, BIBREF12 and BIBREF10, we choose SE07, the smallest among these test sets, as the development set 
Experiments ::: Datasets ::: WordNet
Since BIBREF17 map all the sense annotations in these datasets from their original versions to WordNet 3 0, we extract word sense glosses from WordNet 3 0 
Experiments ::: Settings
We use the pre-trained uncased BERT$_\mathrm {BASE}$ model for fine-tuning, because we find that BERT$_\mathrm {LARGE}$ model performs slightly worse than BERT$_\mathrm {BASE}$ in this task  The number of Transformer blocks is 12, the number of the hidden layer is 768, the number of self-attention heads is 12, and the total number of parameters of the pre-trained model is 110M  When fine-tuning, we use the development set (SE07) to find the optimal settings for our experiments  We keep the dropout probability at 0 1, set the number of epochs to 4  The initial learning rate is 2e-5, and the batch size is 64 
Experiments ::: Results
Table TABREF19 shows the performance of our method on the English all-words WSD benchmark datasets  We compare our approach with previous methods 
The first block shows the MFS baseline, which selects the most frequent sense in the training corpus for each target word 
The second block shows two knowledge-based systems  Lesk$_{ext+emb}$ BIBREF4 is a variant of Lesk algorithm BIBREF2 by calculating the gloss-context overlap of the target word  Babelfy BIBREF6 is a unified graph-based approach which exploits the semantic network structure from BabelNet 
The third block shows two word expert traditional supervised systems  IMS BIBREF7 is a flexible framework which trains SVM classifiers and uses local features  And IMS$_{+emb}$ BIBREF9 is the best configuration of the IMS framework, which also integrates word embeddings as features 
The fourth block shows several recent neural-based methods  Bi-LSTM BIBREF11 is a baseline for neural models  Bi-LSTM$_{+ att  + LEX + POS}$ BIBREF10 is a multi-task learning framework for WSD, POS tagging, and LEX with self-attention mechanism, which converts WSD to a sequence learning task  GAS$_{ext}$ BIBREF12 is a variant of GAS which is a gloss-augmented variant of the memory network by extending gloss knowledge  CAN$^s$ and HCAN BIBREF13 are sentence-level and hierarchical co-attention neural network models which leverage gloss knowledge 
In the last block, we report the performance of our method  BERT(Token-CLS) is our baseline, which does not incorporate gloss information, and it performs slightly worse than previous traditional supervised methods and recent neural-based methods  It proves that directly using BERT cannot obtain performance growth  The other three methods outperform other models by a substantial margin, which proves that the improvements come from leveraging BERT to better exploit gloss information  It is worth noting that our method achieves significant improvements in SE07 and Verb over previous methods, which have the highest ambiguity level among all datasets and all POS tags respectively according to BIBREF17 
Moreover, GlossBERT(Token-CLS) performs better than GlossBERT(Sent-CLS), which proves that highlighting the target word in the sentence is important  However, the weakly highlighting method GlossBERT(Sent-CLS-WS) performs best in most circumstances, which may result from its combination of the advantages of the other two methods 
Experiments ::: Discussion
There are two main reasons for the great improvements of our experimental results  First, we construct context-gloss pairs and convert WSD problem to a sentence-pair classification task which is similar to NLI tasks and train only one classifier, which is equivalent to expanding the corpus  Second, we leverage BERT BIBREF15 to better exploit the gloss information  BERT model shows its advantage in dealing with sentence-pair classification tasks by its amazing improvement on QA and NLI tasks  This advantage comes from both of its two novel unsupervised prediction tasks 
Compared with traditional word expert supervised methods, our GlossBERT shows its effectiveness to alleviate the effort of feature engineering and does not require training a dedicated classifier for every target lemma  Up to now, it can be said that the neural network method can totally beat the traditional word expert method  Compared with recent neural-based methods, our solution is more intuitive and can make better use of gloss knowledge  Besides, our approach demonstrates that when we fine-tune BERT on a downstream task, converting it into a sentence-pair classification task may be a good choice 
Conclusion
In this paper, we seek to better leverage gloss knowledge in a supervised neural WSD system  We propose a new solution to WSD by constructing context-gloss pairs and then converting WSD to a sentence-pair classification task  We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task 
Acknowledgments
We would like to thank the anonymous reviewers for their valuable comments  The research work is supported by National Natural Science Foundation of China (No  61751201 and 61672162), Shanghai Municipal Science and Technology Commission (16JC1420401 and 17JC1404100), Shanghai Municipal Science and Technology Major Project (No 2018SHZDZX01) and ZJLab 

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. Do they use large or small BERT?
Context 3: Introduction
Neural networks have been successfully used to describe images with text using sequence-to-sequence models BIBREF0  However, the results are simple and dry captions which are one or two phrases long  Humans looking at a painting see more than just objects  Paintings stimulate sentiments, metaphors and stories as well  Therefore, our goal is to have a neural network describe the painting artistically in a style of choice  As a proof of concept, we present a model which generates Shakespearean prose for a given painting as shown in Figure FIGREF1  Accomplishing this task is difficult with traditional sequence to sequence models since there does not exist a large collection of Shakespearean prose which describes paintings: Shakespeare's works describes a single painting shown in Figure FIGREF3  Fortunately we have a dataset of modern English poems which describe images BIBREF1 and line-by-line modern paraphrases of Shakespeare's plays BIBREF2  Our solution is therefore to combine two separately trained models to synthesize Shakespearean prose for a given painting 
Introduction ::: Related work
A general end-to-end approach to sequence learning BIBREF3 makes minimal assumptions on the sequence structure  This model is widely used in tasks such as machine translation, text summarization, conversational modeling, and image captioning  A generative model using a deep recurrent architecture BIBREF0 has also beeen used for generating phrases describing an image  The task of synthesizing multiple lines of poetry for a given image BIBREF1 is accomplished by extracting poetic clues from images  Given the context image, the network associates image attributes with poetic descriptions using a convolutional neural net  The poem is generated using a recurrent neural net which is trained using multi-adversarial training via policy gradient 
Transforming text from modern English to Shakespearean English using text "style transfer" is challenging  An end to end approach using a sequence-to-sequence model over a parallel text corpus BIBREF2 has been proposed based on machine translation  In the absence of a parallel text corpus, generative adversarial networks (GANs) have been used, which simultaneously train two models: a generative model which captures the data distribution, and a discriminative model which evaluates the performance of the generator  Using a target domain language model as a discriminator has also been employed BIBREF4, providing richer and more stable token-level feedback during the learning process  A key challenge in both image and text style transfer is separating content from style BIBREF5, BIBREF6, BIBREF7  Cross-aligned auto-encoder models have focused on style transfer using non-parallel text BIBREF7  Recently, a fine grained model for text style transfer has been proposed BIBREF8 which controls several factors of variation in textual data by using back-translation  This allows control over multiple attributes, such as gender and sentiment, and fine-grained control over the trade-off between content and style 
Methods
We use a total three datasets: two datasets for generating an English poem from an image, and Shakespeare plays and their English translations for text style transfer 
We train a model for generating poems from images based on two datasets BIBREF1  The first dataset consists of image and poem pairs, namely a multi-modal poem dataset (MultiM-Poem), and the second dataset is a large poem corpus, namely a uni-modal poem dataset (UniM-Poem)  The image and poem pairs are extended by adding the nearest three neighbor poems from the poem corpus without redundancy, and an extended image and poem pair dataset is constructed and denoted as MultiM-Poem(Ex)BIBREF1 
We use a collection of line-by-line modern paraphrases for 16 of Shakespeare’s plays BIBREF2, for training a style transfer network from English poems to Shakespearean prose  We use 18,395 sentences from the training data split  We keep 1,218 sentences in the validation data set and 1,462 sentences in our test set 
Methods ::: Image To Poem Actor-Critic Model
For generating a poem from images we use an existing actor-critic architecture BIBREF1  This involves 3 parallel CNNs: an object CNN, sentiment CNN, and scene CNN, for feature extraction  These features are combined with a skip-thought model which provides poetic clues, which are then fed into a sequence-to-sequence model trained by policy gradient with 2 discriminator networks for rewards  This as a whole forms a pipeline that takes in an image and outputs a poem as shown on the top left of Figure FIGREF4  A CNN-RNN generative model acts as an agent  The parameters of this agent define a policy whose execution determines which word is selected as an action  When the agent selects all words in a poem, it receives a reward  Two discriminative networks, shown on the top right of Figure FIGREF4, are defined to serve as rewards concerning whether the generated poem properly describes the input image and whether the generated poem is poetic  The goal of the poem generation model is to generate a sequence of words as a poem for an image to maximize the expected return 
Methods ::: Shakespearizing Poetic Captions
For Shakespearizing modern English texts, we experimented with various types of sequence to sequence models  Since the size of the parallel translation data available is small, we leverage a dictionary providing a mapping between Shakespearean words and modern English words to retrofit pre-trained word embeddings

ground_truth: small BERT

small BERT

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 limitations do the authors demnostrate of their model?
Context 3:  Incorporating this extra information improves the translation task  The large number of shared word types between the source and target sentences indicates that sharing the representation between them is beneficial 
Methods ::: Shakespearizing Poetic Captions ::: Seq2Seq with Attention
We use a sequence-to-sequence model which consists of a single layer unidrectional LSTM encoder and a single layer LSTM decoder and pre-trained retrofitted word embeddings shared between source and target sentences  We experimented with two different types of attention: global attention BIBREF9, in which the model makes use of the output from the encoder and decoder for the current time step only, and Bahdanau attention BIBREF10, where computing attention requires the output of the decoder from the prior time step  We found that global attention performs better in practice for our task of text style transfer 
Methods ::: Shakespearizing Poetic Captions ::: Seq2Seq with a Pointer Network
Since a pair of corresponding Shakespeare and modern English sentences have significant vocabulary overlap we extend the sequence-to-sequence model mentioned above using pointer networks BIBREF11 that provide location based attention and have been used to enable copying of tokens directly from the input  Moreover, there are lot of proper nouns and rare words which might not be predicted by a vanilla sequence to sequence model 
Methods ::: Shakespearizing Poetic Captions ::: Prediction
For both seq2seq models, we use the attention matrices returned at each decoder time step during inference, to compute the next word in the translated sequence if the decoder output at the current time step is the UNK token  We replace the UNKs in the target output with the highest aligned, maximum attention, source word  The seq2seq model with global attention gives the best results with an average target BLEU score of 29 65 on the style transfer dataset, compared with an average target BLEU score of 26 97 using the seq2seq model with pointer networks 
Results
We perform a qualitative analysis of the Shakespearean prose generated for the input paintings  We conducted a survey, in which we presented famous paintings including those shown in Figures FIGREF1 and FIGREF10 and the corresponding Shakespearean prose generated by the model, and asked 32 students to rate them on the basis of content, creativity and similarity to Shakespearean style on a Likert scale of 1-5  Figure FIGREF12 shows the result of our human evaluation 
The average content score across the paintings is 3 7 which demonstrates that the prose generated is relevant to the painting  The average creativity score is 3 9 which demonstrates that the model captures more than basic objects in the painting successfully using poetic clues in the scene  The average style score is 3 9 which demonstrates that the prose generated is perceived to be in the style of Shakespeare 
We also perform a quantitative analysis of style transfer by generating BLEU scores for the model output using the style transfer dataset  The variation of the BLEU scores with the source sentence lengths is shown in Figure FIGREF11  As expected, the BLEU scores decrease with increase in source sentence lengths 
Results ::: Implementation
All models were trained on Google Colab with a single GPU using Python 3 6 and Tensorflow 2 0  The number of hidden units for the encoder and decoder is 1,576 and 256 for seq2seq with global attention and seq2seq with pointer networks respectively  Adam optimizer was used with the default learning rate of 0 001  The model was trained for 25 epochs  We use pre-trained retrofitted word embeddings of dimension 192 
Results ::: Limitations
Since we do not have an end-to-end dataset, the generated English poem may not work well with Shakespeare style transfer as shown in Figure FIGREF12 for "Starry Night" with a low average content score  This happens when the style transfer dataset does not have similar words in the training set of sentences  A solution would be to expand the style transfer dataset, for a better representation of the poem data 
Results ::: Conclusions and Future Work
In conclusion, combining two pipelines with an intermediate representation works well in practice  We observe that a CNN-RNN based image-to-poem net combined with a seq2seq model with parallel text corpus for text style transfer synthesizes Shakespeare-style prose for a given painting  For the seq2seq model used, we observe that it performs better in practice using global attention as compared with local attention  We make our models and code publicly available BIBREF12  In future work we would like to experiment with GANs in the absence of non-parallel datasets, so that we can use varied styles for text style transfer  We would also like to experiment with cross aligned auto-encoders, which form a latent content representation, to efficiently separate style and content 

ground_truth: Since we do not have an end-to-end dataset, the generated English poem may not work well with Shakespeare style transfer

we do not have an end-to-end dataset, the generated English poem may not work well with Shakespeare style transfer as shown in Figure FIGREF12 for "Starry Night" with a low average content score