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

Please answer the questions based on the text provided below. How do they match words before reordering them?
Context 1: Introduction
Deep Learning approaches have achieved impressive results on various NLP tasks BIBREF0 , BIBREF1 , BIBREF2 and have become the de facto approach for any NLP task  However, these deep learning techniques have found to be less effective for low-resource languages when the available training data is very less BIBREF3   Recently, several approaches like Multi-task learning BIBREF4 , multilingual learning BIBREF5 , semi-supervised learning BIBREF2 , BIBREF6 and transfer learning BIBREF7 , BIBREF3 have been explored by the deep learning community to overcome data sparsity in low-resource languages  Transfer learning trains a model for a parent task and fine-tunes the learned parent model weights (features) for a related child task BIBREF7 , BIBREF8   This effectively reduces the requirement on training data for the child task as the model would have learned relevant features from the parent task data thereby, improving the performance on the child task 
Transfer learning has also been explored in the multilingual Neural Machine Translation BIBREF3 , BIBREF9 , BIBREF10   The goal is to improve the NMT performance on the source to target language pair (child task) using an assisting source language (assisting to target translation is the parent task)  Here, the parent model is trained on the assisting and target language parallel corpus and the trained weights are used to initialize the child model  The child model can now be fine-tuned on the source-target language pairs, if parallel corpus is available  The divergence between the source and the assisting language can adversely impact the benefits obtained from transfer learning  Multiple studies have shown that transfer learning works best when the languages are related BIBREF3 , BIBREF10 , BIBREF9   Several studies have tried to address lexical divergence between the source and the target languages BIBREF10 , BIBREF11 , BIBREF12   However, the effect of word order divergence and its mitigation has not been explored  In a practical setting, it is not uncommon to have source and assisting languages with different word order  For instance, it is possible to find parallel corpora between English and some Indian languages, but very little parallel corpora between Indian languages  Hence, it is natural to use English as an assisting language for inter-Indian language translation 
To see how word order divergence can be detrimental, let us consider the case of the standard RNN (Bi-LSTM) encoder-attention-decoder architecture BIBREF13   The encoder generates contextual representations (annotation vectors) for each source word, which are used by the attention network to match the source words to the current decoder state  The contextual representation is word-order dependent  Hence, if the assisting and the source languages do not have similar word order the generated contextual representations will not be consistent  The attention network (and hence the decoder) sees different contextual representations for similar words in parallel sentences across different languages  This makes it difficult to transfer knowledge learned from the assisting language to the source language 
We illustrate this by visualizing the contextual representations generated by the encoder of an English to Hindi NMT system for two versions of the English input: (a) original word order (SVO) (b) word order of the source language (SOV, for Bengali)  Figure FIGREF1 shows that the encoder representations obtained are very different  The attention network and the decoder now have to work with very different representations  Note that the plot below does not take into account further lexical and other divergences between source and assisting languages, since we demonstrated word order divergence with the same language on the source side 
To address this word order divergence, we propose to pre-order the assisting language sentences to match the word order of the source language  We consider an extremely resource constrained scenario, where we do not have any parallel corpus for the child task  We are limited to a bilingual dictionary for transfer information from the assisting to the source language  From our experiments, we show that there is a significant increase in the translation accuracy for the unseen source-target language pair 
Addressing Lexical Divergence
BIBREF3 explored transfer learning for NMT on low-resource languages  They studied the influence of language divergence between languages chosen for training the parent and child model, and showed that choosing similar languages for training the parent and child model leads to better improvements from transfer learning  A limitation of BIBREF3 approach is that they ignore the lexical similarity between languages and also the source language embeddings are randomly initialized  BIBREF10 , BIBREF11 , BIBREF12 take advantage of lexical similarity between languages in their work  BIBREF10 proposed to use Byte-Pair Encoding (BPE) to represent the sentences in both the parent and the child language to overcome the above limitation  They show using BPE benefits transfer learning especially when the involved languages are closely-related agglutinative languages  Similarly, BIBREF11 utilize lexical similarity between the source and assisting languages by training a character-level NMT system  BIBREF12 address lexical divergence by using bilingual embeddings and mixture of universal token embeddings  One of the languages' vocabulary, usually English vocabulary is considered as universal tokens and every word in the other languages is represented as a mixture of universal tokens  They show results on extremely low-resource languages 
Addressing Word Order Divergence
To the best of our knowledge, no work has addressed word order divergence in transfer learning for multilingual NMT  However, some work exists for other NLP tasks that could potentially address word order  For Named Entity Recognition (NER), BIBREF14 use a self-attention layer after the Bi-LSTM layer to address word-order divergence for Named Entity Recognition (NER) task  The approach does not show any significant improvements over multiple languages  A possible reason is that the divergence has to be addressed before/during construction of the contextual embeddings in the Bi-LSTM layer, and the subsequent self-attention layer does not address word-order divergence  BIBREF15 use adversarial training for cross-lingual question-question similarity ranking in community question answering  The adversarial training tries to force the encoder representations of similar sentences from different input languages to have similar representations 
Use of Pre-ordering
Pre-ordering the source language sentences to match the target language word order has been useful in addressing word-order divergence for Phrase-Based SMT BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19   Recently, BIBREF20 proposed a way to measure and reduce the divergence between the source and target languages based on morphological and syntactic properties, also termed as anisomorphism  They demonstrated that by reducing the anisomorphism between the source and target languages, consistent improvements in NMT performance were obtained  The NMT system used additional features like word forms, POS tags and dependency relations in addition to parallel corpora  On the other hand, BIBREF21 observed a drop in performance due to pre-ordering for NMT  Unlike BIBREF20 , the NMT system was trained on pre-ordered sentences and no additional features were provided to the system  Note that all these works address source-target divergence, not divergence between source languages in multilingual NMT 
Proposed Solution
Consider the task of translating from an extremely low-resource language (source) to a target language  The parallel corpus between the two languages if available may be too small to train a NMT model  Similar to existing works BIBREF3 , BIBREF10 , BIBREF12 , we use transfer learning to overcome data sparsity and train a NMT model between the source and the target languages  Specifically, the NMT model (parent model) is trained on the assisting language and target language pairs  We choose English as the assisting language in all our experiments  In our resource-scarce scenario, we have no parallel corpus for the child task  Hence, at test time, the source language sentence is translated using the parent model after performing a word-by-word translation into the assisting language 
Since the source language and the assisting language (English) have different word order, we hypothesize that it leads to inconsistencies in the contextual representations generated by the encoder for the two languages  In this paper, we propose to pre-order English sentences (assisting language sentences) to match the word-order of the source language and train the parent model on this pre-ordered corpus  In our experiments, we look at scenarios where the assisting language has SVO word order and the source language has SOV word order 
For instance, consider the English sentence Anurag will meet Thakur  One of the pre-ordering rule swaps the position of the noun phrase followed by a transitive verb with the transitive verb  The original and the resulting re-ordered parse tree will be as shown in the Table TABREF5   Applying this reordering rule to the above sentence Anurag will meet Thakur will yield the reordered sentence Anurag Thakur will meet  Additionally, the Table TABREF5 shows the parse trees for the above sentence with and without pre-ordering 
Pre-ordering should also be beneficial for other word order divergence scenarios (e g , SOV to SVO), but we leave verification of these additional scenarios for future work 
Experimental Setup
In this section, we describe the languages experimented with, datasets used, the network hyper-parameters used in our experiments 
Languages
We experimented with English INLINEFORM0 Hindi translation as the parent task  English is the assisting source language  Bengali, Gujarati, Marathi, Malayalam and Tamil are the primary source languages, and translation from these to Hindi constitute the child tasks  Hindi, Bengali, Gujarati and Marathi are Indo-Aryan languages, while Malayalam and Tamil are Dravidian languages  All these languages have a canonical SOV word order 
Datasets
For training English-Hindi NMT systems, we use the IITB English-Hindi parallel corpus BIBREF22 ( INLINEFORM0 sentences from the training set) and the ILCI English-Hindi parallel corpus ( INLINEFORM1 sentences)  The ILCI (Indian Language Corpora Initiative) multilingual parallel corpus BIBREF23 spans multiple Indian languages from the health and tourism domains  We use the 520-sentence dev-set of the IITB parallel corpus for validation  For each child task, we use INLINEFORM2 sentences from ILCI corpus as the test set 
Network
We use OpenNMT-Torch BIBREF24 to train the NMT system  We use the standard sequence-to-sequence architecture with attention BIBREF13   We use an encoder which contains two layers of bidirectional LSTMs with 500 neurons each  The decoder contains two LSTM layers with 500 neurons each  Input feeding approach BIBREF1 is used where the previous attention hidden state is fed as input to the decoder LSTM  We use a mini-batch of size 50 and use a dropout layer  We begin with an initial learning rate of INLINEFORM0 and decay the learning rate by a factor of INLINEFORM1 when the perplexity on validation set increases  The training is stopped when the learning rate falls below INLINEFORM2 or number of epochs=22  The English input is initialized with pre-trained embeddings trained using fastText BIBREF25  
English vocabulary consists of INLINEFORM0 tokens appearing at least 2 times in the English training corpus  For constructing the Hindi vocabulary we considered only those tokens appearing at least 5 times in the training split resulting in a vocabulary size of INLINEFORM1 tokens  For representing English and other source languages into a common space, we translate each word in the source language into English using a bilingual dictionary (Google Translate word translation in our case)  In an end-to-end solution, it would have been ideal to use bilingual embeddings or obtain word-by-word translations via bilingual embeddings BIBREF14   But, the quality of publicly available bilingual embeddings for English-Indian languages is very low for obtaining good-quality, bilingual representations BIBREF26 , BIBREF27   We also found that these embeddings were not useful for transfer learning 
We use the CFILT-preorder system for reordering English sentences to match the Indian language word order  It contains two re-ordering systems: (1) generic rules that apply to all Indian languages BIBREF17 , and (2) hindi-tuned rules which improve the generic rules by incorporating improvements found through an error analysis of English-Hindi reordering BIBREF28   These Hindi-tuned rules have been found to improve reordering for many English to Indian language pairs BIBREF29  
Results
In this section, we describe the results from our experiments on NMT task  We report the results on X-Hindi pair, where X is one of Bengali, Gujarati, Marathi, Tamil, and Malayalam  The results are presented in the Table TABREF6   We report BLEU scores and LeBLEU scores BIBREF30   We observe that both the pre-ordering configurations significantly improve the BLEU scores over the baseline scores  We observe larger gains when generic pre-ordering rules are used compared to the Hindi-tuned pre-ordering rules 
These results support our hypothesis that word-order divergence can limit the benefits of multilingual translation  Reducing the word order divergence can improve translation in extremely low-resource scenarios 
An analysis of the outputs revealed that pre-ordering significantly reducing the number of UNK tokens (placeholder for unknown words) in the test output (Table TABREF14 )  We hypothesize that due to word order divergence between English and Indian languages, the encoder representation generated is not consistent leading to decoder generating unknown words  However, the pre-ordered models generate better contextual representations leading to less number of unknown tokens and better translation which is also reflected in the BLEU scores 
Conclusion
In this paper, we show that handling word-order divergence between source and assisting languages is crucial for the success of multilingual NMT in an extremely low-resource setting  We show that pre-ordering the assisting language to match the word order of the source language significantly improves translation quality in an extremely low-resource setting  While the current work focused on Indian languages, we would like to validate the hypothesis on a more diverse set of languages 
Context 2: Introduction
There exists a class of language construction known as pun in natural language texts and utterances, where a certain word or other lexical items are used to exploit two or more separate meanings  It has been shown that understanding of puns is an important research question with various real-world applications, such as human-computer interaction BIBREF0 , BIBREF1 and machine translation BIBREF2   Recently, many researchers show their interests in studying puns, like detecting pun sentences BIBREF3 , locating puns in the text BIBREF4 , interpreting pun sentences BIBREF5 and generating sentences containing puns BIBREF6 , BIBREF7 , BIBREF8   A pun is a wordplay in which a certain word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another sign, for an intended humorous or rhetorical effect  Puns can be generally categorized into two groups, namely heterographic puns (where the pun and its latent target are phonologically similar) and homographic puns (where the two meanings of the pun reflect its two distinct senses) BIBREF9   Consider the following two examples:
The first punning joke exploits the sound similarity between the word “propane" and the latent target “profane", which can be categorized into the group of heterographic puns  Another categorization of English puns is homographic pun, exemplified by the second instance leveraging distinct senses of the word “gut" 
Pun detection is the task of detecting whether there is a pun residing in the given text  The goal of pun location is to find the exact word appearing in the text that implies more than one meanings  Most previous work addresses such two tasks separately and develop separate systems BIBREF10 , BIBREF5   Typically, a system for pun detection is built to make a binary prediction on whether a sentence contains a pun or not, where all instances (with or without puns) are taken into account during training  For the task of pun location, a separate system is used to make a single prediction as to which word in the given sentence in the text that trigger more than one semantic interpretations of the text, where the training data involves only sentences that contain a pun  Therefore, if one is interested in solving both problems at the same time, a pipeline approach that performs pun detection followed by pun location can be used 
Compared to the pipeline methods, joint learning has been shown effective BIBREF11 , BIBREF12 since it is able to reduce error propagation and allows information exchange between tasks which is potentially beneficial to all the tasks  In this work, we demonstrate that the detection and location of puns can be jointly addressed by a single model  The pun detection and location tasks can be combined as a sequence labeling problem, which allows us to jointly detect and locate a pun in a sentence by assigning each word a tag  Since each context contains a maximum of one pun BIBREF9 , we design a novel tagging scheme to capture this structural constraint  Statistics on the corpora also show that a pun tends to appear in the second half of a context  To capture such a structural property, we also incorporate word position knowledge into our structured prediction model  Experiments on the benchmark datasets show that detection and location tasks can reinforce each other, leading to new state-of-the-art performance on these two tasks  To the best of our knowledge, this is the first work that performs joint detection and location of English puns by using a sequence labeling approach 
Problem Definition
We first design a simple tagging scheme consisting of two tags { INLINEFORM0 }:
INLINEFORM0 tag means the current word is not a pun 
INLINEFORM0 tag means the current word is a pun 
If the tag sequence of a sentence contains a INLINEFORM0 tag, then the text contains a pun and the word corresponding to INLINEFORM1 is the pun 
The contexts have the characteristic that each context contains a maximum of one pun BIBREF9   In other words, there exists only one pun if the given sentence is detected as the one containing a pun  Otherwise, there is no pun residing in the text  To capture this interesting property, we propose a new tagging scheme consisting of three tags, namely { INLINEFORM0 } 
INLINEFORM0 tag indicates that the current word appears before the pun in the given context 
INLINEFORM0 tag highlights the current word is a pun 
INLINEFORM0 tag indicates that the current word appears after the pun 
We empirically show that the INLINEFORM0 scheme can guarantee the context property that there exists a maximum of one pun residing in the text 
Given a context from the training set, we will be able to generate its corresponding gold tag sequence using a deterministic procedure  Under the two schemes, if a sentence does not contain any puns, all words will be tagged with INLINEFORM0 or INLINEFORM1 , respectively  Exemplified by the second sentence “Some diets cause a gut reaction," the pun is given as “gut " Thus, under the INLINEFORM2 scheme, it should be tagged with INLINEFORM3 , while the words before it are assigned with the tag INLINEFORM4 and words after it are with INLINEFORM5 , as illustrated in Figure FIGREF8   Likewise, the INLINEFORM6 scheme tags the word “gut" with INLINEFORM7 , while other words are tagged with INLINEFORM8   Therefore, we can combine the pun detection and location tasks into one problem which can be solved by the sequence labeling approach 
Model
Neural models have shown their effectiveness on sequence labeling tasks BIBREF13 , BIBREF14 , BIBREF15   In this work, we adopt the bidirectional Long Short Term Memory (BiLSTM) BIBREF16 networks on top of the Conditional Random Fields BIBREF17 (CRF) architecture to make labeling decisions, which is one of the classical models for sequence labeling  Our model architecture is illustrated in Figure FIGREF8 with a running example  Given a context/sentence INLINEFORM0 where INLINEFORM1 is the length of the context, we generate the corresponding tag sequence INLINEFORM2 based on our designed tagging schemes and the original annotations for pun detection and location provided by the corpora  Our model is then trained on pairs of INLINEFORM3  
Input  The contexts in the pun corpus hold the property that each pun contains exactly one content word, which can be either a noun, a verb, an adjective, or an adverb  To capture this characteristic, we consider lexical features at the character level  Similar to the work of BIBREF15 , the character embeddings are trained by the character-level LSTM networks on the unannotated input sequences  Nonlinear transformations are then applied to the character embeddings by highway networks BIBREF18 , which map the character-level features into different semantic spaces 
We also observe that a pun tends to appear at the end of a sentence  Specifically, based on the statistics, we found that sentences with a pun that locate at the second half of the text account for around 88% and 92% in homographic and heterographic datasets, respectively  We thus introduce a binary feature that indicates if a word is located at the first or the second half of an input sentence to capture such positional information  A binary indicator can be mapped to a vector representation using a randomly initialized embedding table BIBREF19 , BIBREF20   In this work, we directly adopt the value of the binary indicator as part of the input 
The concatenation of the transformed character embeddings, the pre-trained word embeddings BIBREF21 , and the position indicators are taken as input of our model 
Tagging  The input is then fed into a BiLSTM network, which will be able to capture contextual information  For a training instance INLINEFORM0 , we suppose the output by the word-level BiLSTM is INLINEFORM1   The CRF layer is adopted to capture label dependencies and make final tagging decisions at each position, which has been included in many state-of-the-art sequence labeling models BIBREF14 , BIBREF15   The conditional probability is defined as:
where INLINEFORM0 is a set of all possible label sequences consisting of tags from INLINEFORM1 (or INLINEFORM2 ), INLINEFORM3 and INLINEFORM4 are weight and bias parameters corresponding to the label pair INLINEFORM5   During training, we minimize the negative log-likelihood summed over all training instances:
where INLINEFORM0 refers to the INLINEFORM1 -th instance in the training set  During testing, we aim to find the optimal label sequence for a new input INLINEFORM2 :
This search process can be done efficiently using the Viterbi algorithm 
Datasets and Settings
We evaluate our model on two benchmark datasets BIBREF9   The homographic dataset contains 2,250 contexts, 1,607 of which contain a pun  The heterographic dataset consists of 1,780 contexts with 1,271 containing a pun  We notice there is no standard splitting information provided for both datasets  Thus we apply 10-fold cross validation  To make direct comparisons with prior studies, following BIBREF4 , we accumulated the predictions for all ten folds and calculate the scores in the end 
For each fold, we randomly select 10% of the instances from the training set for development  Word embeddings are initialized with the 100-dimensional Glove BIBREF21   The dimension of character embeddings is 30 and they are randomly initialized, which can be fine tuned during training  The pre-trained word embeddings are not updated during training  The dimensions of hidden vectors for both char-level and word-level LSTM units are set to 300  We adopt stochastic gradient descent (SGD) BIBREF26 with a learning rate of 0 015 
For the pun detection task, if the predicted tag sequence contains at least one INLINEFORM0 tag, we regard the output (i e , the prediction of our pun detection model) for this task as true, otherwise false  For the pun location task, a predicted pun is regarded as correct if and only if it is labeled as the gold pun in the dataset  As to pun location, to make fair comparisons with prior studies, we only consider the instances that are labeled as the ones containing a pun  We report precision, recall and INLINEFORM1 score in Table TABREF11   A list of prior works that did not employ joint learning are also shown in the first block of Table TABREF11  
Results
We also implemented a baseline model based on conditional random fields (CRF), where features like POS tags produced by the Stanford POS tagger BIBREF27 , n-grams, label transitions, word suffixes and relative position to the end of the text are considered  We can see that our model with the INLINEFORM0 tagging scheme yields new state-of-the-art INLINEFORM1 scores on pun detection and competitive results on pun location, compared to baselines that do not adopt joint learning in the first block  For location on heterographic puns, our model's performance is slightly lower than the system of BIBREF25 , which is a rule-based locator  Compared to CRF, we can see that our model, either with the INLINEFORM2 or the INLINEFORM3 scheme, yields significantly higher recall on both detection and location tasks, while the precisions are relatively close  This demonstrates the effectiveness of BiLSTM, which learns the contextual features of given texts – such information appears to be helpful in recalling more puns 
Compared to the INLINEFORM0 scheme, the INLINEFORM1 tagging scheme is able to yield better performance on these two tasks  After studying outputs from these two approaches, we found that one leading source of error for the INLINEFORM2 approach is that there exist more than one words in a single instance that are assigned with the INLINEFORM3 tag  However, according to the description of pun in BIBREF9 , each context contains a maximum of one pun  Thus, such a useful structural constraint is not well captured by the simple approach based on the INLINEFORM4 tagging scheme  On the other hand, by applying the INLINEFORM5 tagging scheme, such a constraint is properly captured in the model  As a result, the results for such a approach are significantly better than the approach based on the INLINEFORM6 tagging scheme, as we can observe from the table  Under the same experimental setup, we also attempted to exclude word position features  Results are given by INLINEFORM7 - INLINEFORM8   It is expected that the performance of pun location drops, since such position features are able to capture the interesting property that a pun tends to appear in the second half of a sentence  While such knowledge is helpful for the location task, interestingly, a model without position knowledge yields improved performance on the pun detection task  One possible reason is that detecting whether a sentence contains a pun is not concerned with such word position information 
Additionally, we conduct experiments over sentences containing a pun only, namely 1,607 and 1,271 instances from homographic and heterographic pun corpora separately  It can be regarded as a “pipeline” method where the classifier for pun detection is regarded as perfect  Following the prior work of BIBREF4 , we apply 10-fold cross validation  Since we are given that all input sentences contain a pun, we only report accumulated results on pun location, denoted as Pipeline in Table TABREF11   Compared with our approaches, the performance of such an approach drops significantly  On the other hand, such a fact demonstrates that the two task, detection and location of puns, can reinforce each other  These figures demonstrate the effectiveness of our sequence labeling method to detect and locate English puns in a joint manner 
Error Analysis
We studied the outputs from our system and make some error analysis  We found the errors can be broadly categorized into several types, and we elaborate them here  1) Low word coverage: since the corpora are relatively small, there exist many unseen words in the test set  Learning the representations of such unseen words is challenging, which affects the model's performance  Such errors contribute around 40% of the total errors made by our system  2) Detection errors: we found many errors are due to the model's inability to make correct pun detection  Such inability harms both pun detection and pun location  Although our approach based on the INLINEFORM0 tagging scheme yields relatively higher scores on the detection task, we still found that 40% of the incorrectly predicted instances fall into this group  3) Short sentences: we found it was challenging for our model to make correct predictions when the given text is short  Consider the example “Superglue! Tom rejoined," here the word rejoined is the corresponding pun  However, it would be challenging to figure out the pun with such limited contextual information 
Related Work
Most existing systems address pun detection and location separately  BIBREF22 applied word sense knowledge to conduct pun detection  BIBREF24 trained a bidirectional RNN classifier for detecting homographic puns  Next, a knowledge-based approach is adopted to find the exact pun  Such a system is not applicable to heterographic puns  BIBREF28 applied Google n-gram and word2vec to make decisions  The phonetic distance via the CMU Pronouncing Dictionary is computed to detect heterographic puns  BIBREF10 used the hidden Markov model and a cyclic dependency network with rich features to detect and locate puns  BIBREF23 used a supervised approach to pun detection and a weakly supervised approach to pun location based on the position within the context and part of speech features  BIBREF25 proposed a rule-based system for pun location that scores candidate words according to eleven simple heuristics  Two systems are developed to conduct detection and location separately in the system known as UWAV BIBREF3   The pun detector combines predictions from three classifiers  The pun locator considers word2vec similarity between every pair of words in the context and position to pinpoint the pun  The state-of-the-art system for homographic pun location is a neural method BIBREF4 , where the word senses are incorporated into a bidirectional LSTM model  This method only supports the pun location task on homographic puns  Another line of research efforts related to this work is sequence labeling, such as POS tagging, chunking, word segmentation and NER  The neural methods have shown their effectiveness in this task, such as BiLSTM-CNN BIBREF13 , GRNN BIBREF29 , LSTM-CRF BIBREF30 , LSTM-CNN-CRF BIBREF14 , LM-LSTM-CRF BIBREF15  
In this work, we combine pun detection and location tasks as a single sequence labeling problem  Inspired by the work of BIBREF15 , we also adopt a LSTM-CRF with character embeddings to make labeling decisions 
Conclusion
In this paper, we propose to perform pun detection and location tasks in a joint manner from a sequence labeling perspective  We observe that each text in our corpora contains a maximum of one pun  Hence, we design a novel tagging scheme to incorporate such a constraint  Such a scheme guarantees that there is a maximum of one word that will be tagged as a pun during the testing phase  We also found the interesting structural property such as the fact that most puns tend to appear at the second half of the sentences can be helpful for such a task, but was not explored in previous works  Furthermore, unlike many previous approaches, our approach, though simple, is generally applicable to both heterographic and homographic puns  Empirical results on the benchmark datasets prove the effectiveness of the proposed approach that the two tasks of pun detection and location can be addressed by a single model from a sequence labeling perspective 
Future research includes the investigations on how to make use of richer semantic and linguistic information for detection and location of puns  Research on puns for other languages such as Chinese is still under-explored, which could also be an interesting direction for our future studies 
Acknowledgments
We would like to thank the three anonymous reviewers for their thoughtful and constructive comments  This work is supported by Singapore Ministry of Education Academic Research Fund (AcRF) Tier 2 Project MOE2017-T2-1-156, and is partially supported by SUTD project PIE-SGP-AI-2018-01 
Context 3: Introduction
Measures of semantic similarity and relatedness quantify the degree to which two concepts are similar (e g , INLINEFORM0 – INLINEFORM1 ) or related (e g , INLINEFORM2 – INLINEFORM3 )  Semantic similarity can be viewed as a special case of semantic relatedness – to be similar is one of many ways that a pair of concepts may be related  The automated discovery of groups of semantically similar or related terms is critical to improving the retrieval BIBREF0 and clustering BIBREF1 of biomedical and clinical documents, and the development of biomedical terminologies and ontologies BIBREF2  
There is a long history in using distributional methods to discover semantic similarity and relatedness (e g , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 )  These methods are all based on the distributional hypothesis, which holds that two terms that are distributionally similar (i e , used in the same context) will also be semantically similar BIBREF7 , BIBREF8   Recently word embedding techniques such as word2vec BIBREF9 have become very popular  Despite the prominent role that neural networks play in many of these approaches, at their core they remain distributional techniques that typically start with a word by word co–occurrence matrix, much like many of the more traditional approaches 
However, despite these successes distributional methods do not perform well when data is very sparse (which is common)  One possible solution is to use second–order co–occurrence vectors BIBREF10 , BIBREF11   In this approach the similarity between two words is not strictly based on their co–occurrence frequencies, but rather on the frequencies of the other words which occur with both of them (i e , second order co–occurrences)  This approach has been shown to be successful in quantifying semantic relatedness BIBREF12 , BIBREF13   However, while more robust in the face of sparsity, second–order methods can result in significant amounts of noise, where contextual information that is overly general is included and does not contribute to quantifying the semantic relatedness between the two concepts 
Our goal then is to discover methods that automatically reduce the amount of noise in a second–order co–occurrence vector  We achieve this by incorporating pairwise semantic similarity scores derived from a taxonomy into our second–order vectors, and then using these scores to select only the most semantically similar co–occurrences (thereby reducing noise) 
We evaluate our method on two datasets that have been annotated in multiple ways  One has been annotated for both similarity and relatedness, and the other has been annotated for relatedness by two different types of experts (medical doctors and medical coders)  Our results show that integrating second order co–occurrences with measures of semantic similarity increases correlation with our human reference standards  We also compare our result to a number of other studies which have applied various word embedding methods to the same reference standards we have used  We find that our method often performs at a comparable or higher level than these approaches  These results suggest that our methods of integrating semantic similarity and relatedness values have the potential to improve performance of purely distributional methods 
Similarity and Relatedness Measures
This section describes the similarity and relatedness measures we integrate in our second–order co–occurrence vectors  We use two taxonomies in this study, SNOMED–CT and MeSH  SNOMED–CT (Systematized Nomenclature of Medicine Clinical Terms) is a comprehensive clinical terminology created for the electronic representation of clinical health information  MeSH (Medical Subject Headings) is a taxonomy of biomedical terms developed for indexing biomedical journal articles 
We obtain SNOMED–CT and MeSH via the Unified Medical Language System (UMLS) Metathesaurus (version 2016AA)  The Metathesaurus contains approximately 2 million biomedical and clinical concepts from over 150 different terminologies that have been semi–automatically integrated into a single source  Concepts in the Metathesaurus are connected largely by two types of hierarchical relations: INLINEFORM0 / INLINEFORM1 (PAR/CHD) and INLINEFORM2 / INLINEFORM3 (RB/RN) 
Similarity Measures
Measures of semantic similarity can be classified into three broad categories : path–based, feature–based and information content (IC)  Path–based similarity measures use the structure of a taxonomy to measure similarity – concepts positioned close to each other are more similar than those further apart  Feature–based methods rely on set theoretic measures of overlap between features (union and intersection)  The information content measures quantify the amount of information that a concept provides – more specific concepts have a higher amount of information content 
RadaMBB89 introduce the Conceptual Distance measure  This measure is simply the length of the shortest path between two concepts ( INLINEFORM0 and INLINEFORM1 ) in the MeSH hierarchy  Paths are based on broader than (RB) and narrower than (RN) relations  CaviedesC04 extends this measure to use parent (PAR) and child (CHD) relations  Our INLINEFORM2 measure is simply the reciprocal of this shortest path value (Equation EQREF3 ), so that larger values (approaching 1) indicate a high degree of similarity  DISPLAYFORM0
While the simplicity of INLINEFORM0 is appealing, it can be misleading when concepts are at different levels of specificity  Two very general concepts may have the same path length as two very specific concepts  WuP94 introduce a correction to INLINEFORM1 that incorporates the depth of the concepts, and the depth of their Least Common Subsumer (LCS)  This is the most specific ancestor two concepts share  In this measure, similarity is twice the depth of the two concept's LCS divided by the product of the depths of the individual concepts (Equation EQREF4 )  Note that if there are multiple LCSs for a pair of concepts, the deepest of them is used in this measure  DISPLAYFORM0
ZhongZLY02 take a very similar approach and again scale the depth of the LCS by the sum of the depths of the two concepts (Equation EQREF5 ), where INLINEFORM0   The value of INLINEFORM1 was set to 2 based on their recommendations  DISPLAYFORM0
PekarS02 offer another variation on INLINEFORM0 , where the shortest path of the two concepts to the LCS is used, in addition to the shortest bath between the LCS and the root of the taxonomy (Equation EQREF6 )  DISPLAYFORM0
Feature–based methods represent each concept as a set of features and then measure the overlap or sharing of features to measure similarity  In particular, each concept is represented as the set of their ancestors, and similarity is a ratio of the intersection and union of these features 
MaedcheS01 quantify the similarity between two concepts as the ratio of the intersection over their union as shown in Equation EQREF8   DISPLAYFORM0
BatetSV11 extend this by excluding any shared features (in the numerator) as shown in Equation EQREF9   DISPLAYFORM0
Information content is formally defined as the negative log of the probability of a concept  The effect of this is to assign rare (low probability) concepts a high measure of information content, since the underlying assumption is that more specific concepts are less frequently used than more common ones 
Resnik95 modified this notion of information content in order to use it as a similarity measure  He defines the similarity of two concepts to be the information content of their LCS (Equation EQREF11 )  DISPLAYFORM0
JiangC97, Lin98, and PirroE10 extend INLINEFORM0 by incorporating the information content of the individual concepts in various different ways  Lin98 defines the similarity between two concepts as the ratio of information content of the LCS with the sum of the individual concept's information content (Equation EQREF12 )  Note that INLINEFORM1 has the same form as INLINEFORM2 and INLINEFORM3 , and is in effect using information content as a measure of specificity (rather than depth)  If there is more than one possible LCS, the LCS with the greatest IC is chosen  DISPLAYFORM0
JiangC97 define the distance between two concepts to be the sum of the information content of the two concepts minus twice the information content of the concepts' LCS  We modify this from a distance to a similarity measure by taking the reciprocal of the distance (Equation EQREF13 )  Note that the denominator of INLINEFORM0 is very similar to the numerator of INLINEFORM1 

ground_truth: Unanswerable

CFILT-preorder system

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 is the tagging scheme employed?
Context 3:  DISPLAYFORM0
PirroE10 define the similarity between two concepts as the information content of the two concept's LCS divided by the sum of their individual information content values minus the information content of their LCS (Equation EQREF14 )  Note that INLINEFORM0 can be viewed as a set–theoretic version of INLINEFORM1   DISPLAYFORM0
Information Content
The information content of a concept may be derived from a corpus (corpus–based) or directly from a taxonomy (intrinsic–based)  In this work we focus on corpus–based techniques 
For corpus–based information content, we estimate the probability of a concept INLINEFORM0 by taking the sum of the probability of the concept INLINEFORM1 and the probability its descendants INLINEFORM2 (Equation EQREF16 )  DISPLAYFORM0
The initial probabilities of a concept ( INLINEFORM0 ) and its descendants ( INLINEFORM1 ) are obtained by dividing the number of times each concept and descendant occurs in the corpus, and dividing that by the total numbers of concepts ( INLINEFORM2 ) 
Ideally the corpus from which we are estimating the probabilities of concepts will be sense–tagged  However, sense–tagging is a challenging problem in its own right, and it is not always possible to carry out reliably on larger amounts of text  In fact in this paper we did not use any sense–tagging of the corpus we derived information content from 
Instead, we estimated the probability of a concept by using the UMLSonMedline dataset  This was created by the National Library of Medicine and consists of concepts from the 2009AB UMLS and the counts of the number of times they occurred in a snapshot of Medline taken on 12 January, 2009  These counts were obtained by using the Essie Search Engine BIBREF14 which queried Medline with normalized strings from the 2009AB MRCONSO table in the UMLS  The frequency of a CUI was obtained by aggregating the frequency counts of the terms associated with the CUI to provide a rough estimate of its frequency  The information content measures then use this information to calculate the probability of a concept 
Another alternative is the use of Intrinsic Information Content  It assess the informativeness of concept based on its placement within a taxonomy by considering the number of incoming (ancestors) relative to outgoing (descendant) links BIBREF15 (Equation EQREF17 )  DISPLAYFORM0
where INLINEFORM0 are the number of descendants of concept INLINEFORM1 that are leaf nodes, INLINEFORM2 are the number of concept INLINEFORM3 's ancestors and INLINEFORM4 are the total number of leaf nodes in the taxonomy 
Relatedness Measures
Lesk86 observed that concepts that are related should share more words in their respective definitions than concepts that are less connected  He was able to perform word sense disambiguation by identifying the senses of words in a sentence with the largest number of overlaps between their definitions  An overlap is the longest sequence of one or more consecutive words that occur in both definitions  BanerjeeP03 extended this idea to WordNet, but observed that WordNet glosses are often very short, and did not contain enough information to distinguish between multiple concepts  Therefore, they created a super–gloss for each concept by adding the glosses of related concepts to the gloss of the concept itself (and then finding overlaps) 
PatwardhanP06 adapted this measure to second–order co–occurrence vectors  In this approach, a vector is created for each word in a concept's definition that shows which words co–occur with it in a corpus  These word vectors are averaged to create a single co-occurrence vector for the concept  The similarity between the concepts is calculated by taking the cosine between the concepts second–order vectors  LiuMPMP12 modified and extended this measure to be used to quantify the relatedness between biomedical and clinical terms in the UMLS  The work in this paper can be seen as a further extension of PatwardhanP06 and LiuMPMP12 
Method
In this section, we describe our second–order similarity vector measure  This incorporates both contextual information using the term pair's definition and their pairwise semantic similarity scores derived from a taxonomy  There are two stages to our approach  First, a co–occurrence matrix must be constructed  Second, this matrix is used to construct a second–order co–occurrence vector for each concept in a pair of concepts to be measured for relatedness 
Co–occurrence Matrix Construction
We build an INLINEFORM0 similarity matrix using an external corpus where the rows and columns represent words within the corpus and the element contains the similarity score between the row word and column word using the similarity measures discussed above  If a word maps to more than one possible sense, we use the sense that returns the highest similarity score 
For this paper our external corpus was the NLM 2015 Medline baseline  Medline is a bibliographic database containing over 23 million citations to journal articles in the biomedical domain and is maintained by National Library of Medicine  The 2015 Medline Baseline encompasses approximately 5,600 journals starting from 1948 and contains 23,343,329 citations, of which 2,579,239 contain abstracts  In this work, we use Medline titles and abstracts from 1975 to present day  Prior to 1975, only 2% of the citations contained an abstract  We then calculate the similarity for each bigram in this dataset and include those that have a similarity score greater than a specified threshold on these experiments 
Measure Term Pairs for Relatedness
We obtain definitions for each of the two terms we wish to measure  Due to the sparsity and inconsistencies of the definitions in the UMLS, we not only use the definition of the term (CUI) but also include the definition of its related concepts  This follows the method proposed by PatwardhanP06 for general English and WordNet, and which was adapted for the UMLS and the medical domain by LiuMPMP12  In particular we add the definitions of any concepts connected via a parent (PAR), child (CHD), RB (broader than), RN (narrower than) or TERM (terms associated with CUI) relation  All of the definitions for a term are combined into a single super–gloss  At the end of this process we should have two super–glosses, one for each term to be measured for relatedness 
Next, we process each super–gloss as follows:
We extract a first–order co–occurrence vector for each term in the super–gloss from the co–occurrence matrix created previously 
We take the average of the first order co–occurrence vectors associated with the terms in a super–gloss and use that to represent the meaning of the term  This is a second–order co–occurrence vector 
After a second–order co–occurrence vector has been constructed for each term, then we calculate the cosine between these two vectors to measure the relatedness of the terms 
Data
We use two reference standards to evaluate the semantic similarity and relatedness measures   UMNSRS was annotated for both similarity and relatedness by medical residents  MiniMayoSRS was annotated for relatedness by medical doctors (MD) and medical coders (coder)  In this section, we describe these data sets and describe a few of their differences 
MiniMayoSRS: The MayoSRS, developed by PakhomovPMMRC10, consists of 101 clinical term pairs whose relatedness was determined by nine medical coders and three physicians from the Mayo Clinic  The relatedness of each term pair was assessed based on a four point scale: (4 0) practically synonymous, (3 0) related, (2 0) marginally related and (1 0) unrelated  MiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter–annotator agreement was achieved  The average correlation between physicians is 0 68  The average correlation between medical coders is 0 78  We evaluate our method on the mean of the physician scores, and the mean of the coders scores in this subset in the same manner as reported by PedersenPPC07 
UMNSRS: The University of Minnesota Semantic Relatedness Set (UMNSRS) was developed by PakhomovMALPM10, and consists of 725 clinical term pairs whose semantic similarity and relatedness was determined independently by four medical residents from the University of Minnesota Medical School  The similarity and relatedness of each term pair was annotated based on a continuous scale by having the resident touch a bar on a touch sensitive computer screen to indicate the degree of similarity or relatedness  The Intraclass Correlation Coefficient (ICC) for the reference standard tagged for similarity was 0

ground_truth: A new tagging scheme that tags the words before and after the pun as well as the pun words.

a new tagging scheme consisting of three tags, namely { INLINEFORM0 }

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 embedding techniques are explored in the paper?
Context 3: 47, and 0 50 for relatedness  Therefore, as suggested by Pakhomov and colleagues,we use a subset of the ratings consisting of 401 pairs for the similarity set and 430 pairs for the relatedness set which each have an ICC of 0 73 
Experimental Framework
We conducted our experiments using the freely available open source software package UMLS::Similarity BIBREF16 version 1 47  This package takes as input two terms (or UMLS concepts) and returns their similarity or relatedness using the measures discussed in Section SECREF2  
Correlation between the similarity measures and human judgments were estimated using Spearman's Rank Correlation ( INLINEFORM0 )  Spearman's measures the statistical dependence between two variables to assess how well the relationship between the rankings of the variables can be described using a monotonic function  We used Fisher's r-to-z transformation BIBREF17 to calculate the significance between the correlation results 
Results and Discussion
Table TABREF26 shows the Spearman's Rank Correlation between the human scores from the four reference standards and the scores from the various measures of similarity introduced in Section SECREF2   Each class of measure is followed by the scores obtained when integrating our second order vector approach with these measures of semantic similarity 
Results Comparison
The results for UMNSRS tagged for similarity ( INLINEFORM0 ) and MiniMayoSRS tagged by coders show that all of the second-order similarity vector measures ( INLINEFORM1 ) except for INLINEFORM2 - INLINEFORM3 obtain a higher correlation than the original measures  We found that INLINEFORM4 - INLINEFORM5 and INLINEFORM6 - INLINEFORM7 obtain the highest correlations of all these results with human judgments 
For the UMNSRS dataset tagged for relatedness and MiniMayoSRS tagged by physicians (MD), the original INLINEFORM0 measure obtains a higher correlation than our measure ( INLINEFORM1 ) although the difference is not statistically significant ( INLINEFORM2 ) 
In order to analyze and better understand these results, we filtered the bigram pairs used to create the initial similarity matrix based on the strength of their similarity using the INLINEFORM0 and the INLINEFORM1 measures  Note that the INLINEFORM2 measure holds to a 0 to 1 scale, while INLINEFORM3 ranges from 0 to an unspecified upper bound that is dependent on the size of the corpus from which information content is estimated  As such we use a different range of threshold values for each measure  We discuss the results of this filtering below 
Thresholding Experiments
Table TABREF29 shows the results of applying the threshold parameter on each of the reference standards using the INLINEFORM0 measure  For example, a threshold of 0 indicates that all of the bigrams were included in the similarity matrix; and a threshold of 1 indicates that only the bigram pairs with a similarity score greater than one were included 
These results show that using a threshold cutoff of 2 obtains the highest correlation for the UMNSRS dataset, and that a threshold cutoff of 4 obtains the highest correlation for the MiniMayoSRS dataset  All of the results show an increase in correlation with human judgments when incorporating a threshold cutoff over all of the original measures  The increase in the correlation for the UMNSRS tagged for similarity is statistically significant ( INLINEFORM0 ), however this is not the case for the UMNSRS tagged for relatedness nor for the MiniMayoSRS data 
Similarly, Table TABREF30 shows the results of applying the threshold parameter (T) on each of the reference standards using the INLINEFORM0 measure  Although, unlike INLINEFORM1 whose scores are greater than or equal to 0 without an upper limit, the INLINEFORM2 measure returns scores between 0 and 1 (inclusive)  Therefore, here a threshold of 0 indicates that all of the bigrams were included in the similarity matrix; and a threshold of INLINEFORM3 indicates that only the bigram pairs with a similarity score greater than INLINEFORM4 were included  The results show an increase in accuracy for all of the datasets except for the MiniMayoSRS tagged for physicians  The increase in the results for the UMNSRS tagged for similarity and the MayoSRS is statistically significant ( INLINEFORM5 )  This is not the case for the UMNSRS tagged for relatedness nor the MiniMayoSRS 
Overall, these results indicate that including only those bigrams that have a sufficiently high similarity score increases the correlation results with human judgments, but what quantifies as sufficiently high varies depending on the dataset and measure 
Comparison with Previous Work
Recently, word embeddings BIBREF9 have become a popular method for measuring semantic relatedness in the biomedical domain  This is a neural network based approach that learns a representation of a word by word co–occurrence matrix  The basic idea is that the neural network learns a series of weights (the hidden layer within the neural network) that either maximizes the probability of a word given its context, referred to as the continuous bag of words (CBOW) approach, or that maximizes the probability of the context given a word, referred to as the Skip–gram approach  These approaches have been used in numerous recent papers 
muneeb2015evalutating trained both the Skip–gram and CBOW models over the PubMed Central Open Access (PMC) corpus of approximately 1 25 million articles  They evaluated the models on a subset of the UMNSRS data, removing word pairs that did not occur in their training corpus more than ten times  chiu2016how evaluated both the the Skip–gram and CBOW models over the PMC corpus and PubMed  They also evaluated the models on a subset of the UMNSRS ignoring those words that did not appear in their training corpus  Pakhomov2016corpus trained CBOW model over three different types of corpora: clinical (clinical notes from the Fairview Health System), biomedical (PMC corpus), and general English (Wikipedia)  They evaluated their method using a subset of the UMNSRS restricting to single word term pairs and removing those not found within their training corpus  sajad2015domain trained the Skip–gram model over CUIs identified by MetaMap on the OHSUMED corpus, a collection of 348,566 biomedical research articles  They evaluated the method on the complete UMNSRS, MiniMayoSRS and the MayoSRS datasets; any subset information about the dataset was not explicitly stated therefore we believe a direct comparison may be possible 
In addition, a previous work very closely related to ours is a retrofitting vector method proposed by YuCBJW16 that incorporates ontological information into a vector representation by including semantically related words  In their measure, they first map a biomedical term to MeSH terms, and second build a word vector based on the documents assigned to the respective MeSH term  They then retrofit the vector by including semantically related words found in the Unified Medical Language System  They evaluate their method on the MiniMayoSRS dataset 
Table TABREF31 shows a comparison to the top correlation scores reported by each of these works on the respective datasets (or subsets) they evaluated their methods on  N refers to the number of term pairs in the dataset the authors report they evaluated their method  The table also includes our top scoring results: the integrated vector-res and vector-faith  The results show that integrating semantic similarity measures into second–order co–occurrence vectors obtains a higher or on–par correlation with human judgments as the previous works reported results with the exception of the UMNSRS rel dataset  The results reported by Pakhomov2016corpus and chiu2016how obtain a higher correlation although the results can not be directly compared because both works used different subsets of the term pairs from the UMNSRS dataset 
Conclusion and Future Work
We have presented a method for quantifying the similarity and relatedness between two terms that integrates pair–wise similarity scores into second–order vectors  The goal of this approach is two–fold  First, we restrict the context used by the vector measure to words that exist in the biomedical domain, and second, we apply larger weights to those word pairs that are more similar to each other  Our hypothesis was that this combination would reduce the amount of noise in the vectors and therefore increase their correlation with human judgments  We evaluated our method on datasets that have been manually annotated for relatedness and similarity and found evidence to support this hypothesis  In particular we discovered that guiding the creation of a second–order context vector by selecting term pairs from biomedical text based on their semantic similarity led to improved levels of correlation with human judgment 
We also explored using a threshold cutoff to include only those term pairs that obtained a sufficiently large level of similarity  We found that eliminating less similar pairs improved the overall results (to a point)  In the future, we plan to explore metrics to automatically determine the threshold cutoff appropriate for a given dataset and measure  We also plan to explore additional features that can be integrated with a second–order vector measure that will reduce the noise but still provide sufficient information to quantify relatedness  We are particularly interested in approaches that learn word, phrase, and sentence embeddings from structured corpora such as literature BIBREF23 and dictionary entries BIBREF24   Such embeddings could be integrated into a second–order vector or be used on their own 
Finally, we compared our proposed method to other distributional approaches, focusing on those that used word embeddings  Our results showed that integrating semantic similarity measures into second–order co–occurrence vectors obtains the same or higher correlation with human judgments as do various different word embedding approaches  However, a direct comparison was not possible due to variations in the subsets of the UMNSRS evaluation dataset used  In the future, we would not only like to conduct a direct comparison but also explore integrating semantic similarity into various kinds of word embeddings by training on pair–wise values of semantic similarity as well as co–occurrence statistics 

ground_truth: Skip–gram, CBOW

integrated vector-res, vector-faith, Skip–gram, CBOW