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 other sentence embeddings methods are evaluated?
Context 1: Introduction
In this publication, we present Sentence-BERT (SBERT), a modification of the BERT network using siamese and triplet networks that is able to derive semantically meaningful sentence embeddings  This enables BERT to be used for certain new tasks, which up-to-now were not applicable for BERT  These tasks include large-scale semantic similarity comparison, clustering, and information retrieval via semantic search 
BERT set new state-of-the-art performance on various sentence classification and sentence-pair regression tasks  BERT uses a cross-encoder: Two sentences are passed to the transformer network and the target value is predicted  However, this setup is unsuitable for various pair regression tasks due to too many possible combinations  Finding in a collection of $n=10\,000$ sentences the pair with the highest similarity requires with BERT $n\cdot (n-1)/2=49\,995\,000$ inference computations  On a modern V100 GPU, this requires about 65 hours  Similar, finding which of the over 40 million existent questions of Quora is the most similar for a new question could be modeled as a pair-wise comparison with BERT, however, answering a single query would require over 50 hours 
A common method to address clustering and semantic search is to map each sentence to a vector space such that semantically similar sentences are close  Researchers have started to input individual sentences into BERT and to derive fixed-size sentence embeddings  The most commonly used approach is to average the BERT output layer (known as BERT embeddings) or by using the output of the first token (the [CLS] token)  As we will show, this common practice yields rather bad sentence embeddings, often worse than averaging GloVe embeddings BIBREF2 
To alleviate this issue, we developed SBERT  The siamese network architecture enables that fixed-sized vectors for input sentences can be derived  Using a similarity measure like cosine-similarity or Manhatten / Euclidean distance, semantically similar sentences can be found  These similarity measures can be performed extremely efficient on modern hardware, allowing SBERT to be used for semantic similarity search as well as for clustering  The complexity for finding the most similar sentence pair in a collection of 10,000 sentences is reduced from 65 hours with BERT to the computation of 10,000 sentence embeddings (5 seconds with SBERT) and computing cosine-similarity (0 01 seconds)  By using optimized index structures, finding the most similar Quora question can be reduced from 50 hours to a few milliseconds BIBREF3 
We fine-tune SBERT on NLI data, which creates sentence embeddings that significantly outperform other state-of-the-art sentence embedding methods like InferSent BIBREF4 and Universal Sentence Encoder BIBREF5  On seven Semantic Textual Similarity (STS) tasks, SBERT achieves an improvement of 11 7 points compared to InferSent and 5 5 points compared to Universal Sentence Encoder  On SentEval BIBREF6, an evaluation toolkit for sentence embeddings, we achieve an improvement of 2 1 and 2 6 points, respectively 
SBERT can be adapted to a specific task  It sets new state-of-the-art performance on a challenging argument similarity dataset BIBREF7 and on a triplet dataset to distinguish sentences from different sections of a Wikipedia article BIBREF8 
The paper is structured in the following way: Section SECREF3 presents SBERT, section SECREF4 evaluates SBERT on common STS tasks and on the challenging Argument Facet Similarity (AFS) corpus BIBREF7  Section SECREF5 evaluates SBERT on SentEval  In section SECREF6, we perform an ablation study to test some design aspect of SBERT  In section SECREF7, we compare the computational efficiency of SBERT sentence embeddings in contrast to other state-of-the-art sentence embedding methods 
Related Work
We first introduce BERT, then, we discuss state-of-the-art sentence embedding methods 
BERT BIBREF0 is a pre-trained transformer network BIBREF9, which set for various NLP tasks new state-of-the-art results, including question answering, sentence classification, and sentence-pair regression  The input for BERT for sentence-pair regression consists of the two sentences, separated by a special [SEP] token  Multi-head attention over 12 (base-model) or 24 layers (large-model) is applied and the output is passed to a simple regression function to derive the final label  Using this setup, BERT set a new state-of-the-art performance on the Semantic Textual Semilarity (STS) benchmark BIBREF10  RoBERTa BIBREF1 showed, that the performance of BERT can further improved by small adaptations to the pre-training process  We also tested XLNet BIBREF11, but it led in general to worse results than BERT 
A large disadvantage of the BERT network structure is that no independent sentence embeddings are computed, which makes it difficult to derive sentence embeddings from BERT  To bypass this limitations, researchers passed single sentences through BERT and then derive a fixed sized vector by either averaging the outputs (similar to average word embeddings) or by using the output of the special CLS token (for example: bertsentenceembeddings1,bertsentenceembeddings2,bertsentenceembeddings3)  These two options are also provided by the popular bert-as-a-service-repository  Up to our knowledge, there is so far no evaluation if these methods lead to useful sentence embeddings 
Sentence embeddings are a well studied area with dozens of proposed methods  Skip-Thought BIBREF12 trains an encoder-decoder architecture to predict the surrounding sentences  InferSent BIBREF4 uses labeled data of the Stanford Natural Language Inference dataset BIBREF13 and the Multi-Genre NLI dataset BIBREF14 to train a siamese BiLSTM network with max-pooling over the output  Conneau et al  showed, that InferSent consistently outperforms unsupervised methods like SkipThought  Universal Sentence Encoder BIBREF5 trains a transformer network and augments unsupervised learning with training on SNLI  hill-etal-2016-learning showed, that the task on which sentence embeddings are trained significantly impacts their quality  Previous work BIBREF4, BIBREF5 found that the SNLI datasets are suitable for training sentence embeddings  yang-2018-learning presented a method to train on conversations from Reddit using siamese DAN and siamese transformer networks, which yielded good results on the STS benchmark dataset 
polyencoders addresses the run-time overhead of the cross-encoder from BERT and present a method (poly-encoders) to compute a score between $m$ context vectors and pre-computed candidate embeddings using attention  This idea works for finding the highest scoring sentence in a larger collection  However, poly-encoders have the drawback that the score function is not symmetric and the computational overhead is too large for use-cases like clustering, which would require $O(n^2)$ score computations 
Previous neural sentence embedding methods started the training from a random initialization  In this publication, we use the pre-trained BERT and RoBERTa network and only fine-tune it to yield useful sentence embeddings  This reduces significantly the needed training time: SBERT can be tuned in less than 20 minutes, while yielding better results than comparable sentence embedding methods 
Model
SBERT adds a pooling operation to the output of BERT / RoBERTa to derive a fixed sized sentence embedding  We experiment with three pooling strategies: Using the output of the CLS-token, computing the mean of all output vectors (MEAN-strategy), and computing a max-over-time of the output vectors (MAX-strategy)  The default configuration is MEAN 
In order to fine-tune BERT / RoBERTa, we create siamese and triplet networks BIBREF15 to update the weights such that the produced sentence embeddings are semantically meaningful and can be compared with cosine-similarity 
The network structure depends on the available training data  We experiment with the following structures and objective functions 
Classification Objective Function  We concatenate the sentence embeddings $u$ and $v$ with the element-wise difference $|u-v|$ and multiply it with the trainable weight $W_t \in \mathbb {R}^{3n \times k}$:
where $n$ is the dimension of the sentence embeddings and $k$ the number of labels  We optimize cross-entropy loss  This structure is depicted in Figure FIGREF4 
Regression Objective Function  The cosine-similarity between the two sentence embeddings $u$ and $v$ is computed (Figure FIGREF5)  We use mean-squared-error loss as the objective function 
Triplet Objective Function  Given an anchor sentence $a$, a positive sentence $p$, and a negative sentence $n$, triplet loss tunes the network such that the distance between $a$ and $p$ is smaller than the distance between $a$ and $n$  Mathematically, we minimize the following loss function:
with $s_x$ the sentence embedding for $a$/$n$/$p$, $||\cdot ||$ a distance metric and margin $\epsilon $  Margin $\epsilon $ ensures that $s_p$ is at least $\epsilon $ closer to $s_a$ than $s_n$  As metric we use Euclidean distance and we set $\epsilon =1$ in our experiments 
Model ::: Training Details
We train SBERT on the combination of the SNLI BIBREF13 and the Multi-Genre NLI BIBREF14 dataset  The SNLI is a collection of 570,000 sentence pairs annotated with the labels contradiction, eintailment, and neutral  MultiNLI contains 430,000 sentence pairs and covers a range of genres of spoken and written text  We fine-tune SBERT with a 3-way softmax-classifier objective function for one epoch  We used a batch-size of 16, Adam optimizer with learning rate $2\mathrm {e}{-5}$, and a linear learning rate warm-up over 10% of the training data  Our default pooling strategy is MEAN 
Evaluation - Semantic Textual Similarity
We evaluate the performance of SBERT for common Semantic Textual Similarity (STS) tasks  State-of-the-art methods often learn a (complex) regression function that maps sentence embeddings to a similarity score  However, these regression functions work pair-wise and due to the combinatorial explosion those are often not scalable if the collection of sentences reaches a certain size  Instead, we always use cosine-similarity to compare the similarity between two sentence embeddings  We ran our experiments also with negative Manhatten and negative Euclidean distances as similarity measures, but the results for all approaches remained roughly the same 
Evaluation - Semantic Textual Similarity ::: Unsupervised STS
We evaluate the performance of SBERT for STS without using any STS specific training data  We use the STS tasks 2012 - 2016 BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, the STS benchmark BIBREF10, and the SICK-Relatedness dataset BIBREF21  These datasets provide labels between 0 and 5 on the semantic relatedness of sentence pairs  We showed in BIBREF22 that Pearson correlation is badly suited for STS  Instead, we compute the Spearman's rank correlation between the cosine-similarity of the sentence embeddings and the gold labels  The setup for the other sentence embedding methods is equivalent, the similarity is computed by cosine-similarity  The results are depicted in Table TABREF6 
The results shows that directly using the output of BERT leads to rather poor performances  Averaging the BERT embeddings achieves an average correlation of only 54 81, and using the CLS-token output only achieves an average correlation of 29 19  Both are worse than computing average GloVe embeddings 
Using the described siamese network structure and fine-tuning mechanism substantially improves the correlation, outperforming both InferSent and Universal Sentence Encoder substantially  The only dataset where SBERT performs worse than Universal Sentence Encoder is SICK-R  Universal Sentence Encoder was trained on various datasets, including news, question-answer pages and discussion forums, which appears to be more suitable to the data of SICK-R  In contrast, SBERT was pre-trained only on Wikipedia (via BERT) and on NLI data 
While RoBERTa was able to improve the performance for several supervised tasks, we only observe minor difference between SBERT and SRoBERTa for generating sentence embeddings 
Evaluation - Semantic Textual Similarity ::: Supervised STS
The STS benchmark (STSb) BIBREF10 provides is a popular dataset to evaluate supervised STS systems  The data includes 8,628 sentence pairs from the three categories captions, news, and forums  It is divided into train (5,749), dev (1,500) and test (1,379)  BERT set a new state-of-the-art performance on this dataset by passing both sentences to the network and using a simple regression method for the output 
We use the training set to fine-tune SBERT using the regression objective function  At prediction time, we compute the cosine-similarity between the sentence embeddings  All systems are trained with 10 random seeds to counter variances BIBREF23 
The results are depicted in Table TABREF10  We experimented with two setups: Only training on STSb, and first training on NLI, then training on STSb  We observe that the later strategy leads to a slight improvement of 1-2 points  This two-step approach had an especially large impact for the BERT cross-encoder, which improved the performance by 3-4 points  We do not observe a significant difference between BERT and RoBERTa 
Evaluation - Semantic Textual Similarity ::: Argument Facet Similarity
We evaluate SBERT on the Argument Facet Similarity (AFS) corpus by MisraEW16  The AFS corpus annotated 6,000 sentential argument pairs from social media dialogs on three controversial topics: gun control, gay marriage, and death penalty  The data was annotated on a scale from 0 (“different topic") to 5 (“completely equivalent")  The similarity notion in the AFS corpus is fairly different to the similarity notion in the STS datasets from SemEval  STS data is usually descriptive, while AFS data are argumentative excerpts from dialogs  To be considered similar, arguments must not only make similar claims, but also provide a similar reasoning  Further, the lexical gap between the sentences in AFS is much larger  Hence, simple unsupervised methods as well as state-of-the-art STS systems perform badly on this dataset BIBREF24 
We evaluate SBERT on this dataset in two scenarios: 1) As proposed by Misra et al , we evaluate SBERT using 10-fold cross-validation  A draw-back of this evaluation setup is that it is not clear how well approaches generalize to different topics  Hence, 2) we evaluate SBERT in a cross-topic setup  Two topics serve for training and the approach is evaluated on the left-out topic  We repeat this for all three topics and average the results 
SBERT is fine-tuned using the Regression Objective Function  The similarity score is computed using cosine-similarity based on the sentence embeddings  We also provide the Pearson correlation $r$ to make the results comparable to Misra et al  However, we showed BIBREF22 that Pearson correlation has some serious drawbacks and should be avoided for comparing STS systems  The results are depicted in Table TABREF12 
Unsupervised methods like tf-idf, average GloVe embeddings or InferSent perform rather badly on this dataset with low scores  Training SBERT in the 10-fold cross-validation setup gives a performance that is nearly on-par with BERT 
However, in the cross-topic evaluation, we observe a performance drop of SBERT by about 7 points Spearman correlation  To be considered similar, arguments should address the same claims and provide the same reasoning  BERT is able to use attention to compare directly both sentences (e g  word-by-word comparison), while SBERT must map individual sentences from an unseen topic to a vector space such that arguments with similar claims and reasons are close  This is a much more challenging task, which appears to require more than just two topics for training to work on-par with BERT 
Evaluation - Semantic Textual Similarity ::: Wikipedia Sections Distinction
ein-dor-etal-2018-learning use Wikipedia to create a thematically fine-grained train, dev and test set for sentence embeddings methods  Wikipedia articles are separated into distinct sections focusing on certain aspects  Dor et al  assume that sentences in the same section are thematically closer than sentences in different sections  They use this to create a large dataset of weakly labeled sentence triplets: The anchor and the positive example come from the same section, while the negative example comes from a different section of the same article  For example, from the Alice Arnold article: Anchor: Arnold joined the BBC Radio Drama Company in 1988 , positive: Arnold gained media attention in May 2012 , negative: Balding and Arnold are keen amateur golfers 
We use the dataset from Dor et al  We use the Triplet Objective, train SBERT for one epoch on the about 1 8 Million training triplets and evaluate it on the 222,957 test triplets  Test triplets are from a distinct set of Wikipedia articles  As evaluation metric, we use accuracy: Is the positive example closer to the anchor than the negative example?
Results are presented in Table TABREF14  Dor et al  fine-tuned a BiLSTM architecture with triplet loss to derive sentence embeddings for this dataset  As the table shows, SBERT clearly outperforms the BiLSTM approach by Dor et al 
Evaluation - SentEval
SentEval BIBREF6 is a popular toolkit to evaluate the quality of sentence embeddings  Sentence embeddings are used as features for a logistic regression classifier  The logistic regression classifier is trained on various tasks in a 10-fold cross-validation setup and the prediction accuracy is computed for the test-fold 
The purpose of SBERT sentence embeddings are not to be used for transfer learning for other tasks  Here, we think fine-tuning BERT as described by devlin2018bert for new tasks is the more suitable method, as it updates all layers of the BERT network  However, SentEval can still give an impression on the quality of our sentence embeddings for various tasks 
We compare the SBERT sentence embeddings to other sentence embeddings methods on the following seven SentEval transfer tasks:
MR: Sentiment prediction for movie reviews snippets on a five start scale BIBREF25 
CR: Sentiment prediction of customer product reviews BIBREF26 
SUBJ: Subjectivity prediction of sentences from movie reviews and plot summaries BIBREF27 
MPQA: Phrase level opinion polarity classification from newswire BIBREF28 
SST: Stanford Sentiment Treebank with binary labels BIBREF29 
TREC: Fine grained question-type classification from TREC BIBREF30 
MRPC: Microsoft Research Paraphrase Corpus from parallel news sources BIBREF31 
The results can be found in Table TABREF15  SBERT is able to achieve the best performance in 5 out of 7 tasks  The average performance increases by about 2 percentage points compared to InferSent as well as the Universal Sentence Encoder  Even though transfer learning is not the purpose of SBERT, it outperforms other state-of-the-art sentence embeddings methods on this task 
It appears that the sentence embeddings from SBERT capture well sentiment information: We observe large improvements for all sentiment tasks (MR, CR, and SST) from SentEval in comparison to InferSent and Universal Sentence Encoder 
The only dataset where SBERT is significantly worse than Universal Sentence Encoder is the TREC dataset  Universal Sentence Encoder was pre-trained on question-answering data, which appears to be beneficial for the question-type classification task of the TREC dataset 
Average BERT embeddings or using the CLS-token output from a BERT network achieved bad results for various STS tasks (Table TABREF6), worse than average GloVe embeddings  However, for SentEval, average BERT embeddings and the BERT CLS-token output achieves decent results (Table TABREF15), outperforming average GloVe embeddings  The reason for this are the different setups  For the STS tasks, we used cosine-similarity to estimate the similarities between sentence embeddings  Cosine-similarity treats all dimensions equally  In contrast, SentEval fits a logistic regression classifier to the sentence embeddings  This allows that certain dimensions can have higher or lower impact on the classification result 
We conclude that average BERT embeddings / CLS-token output from BERT return sentence embeddings that are infeasible to be used with cosine-similarity or with Manhatten / Euclidean distance  For transfer learning, they yield slightly worse results than InferSent or Universal Sentence Encoder  However, using the described fine-tuning setup with a siamese network structure on NLI datasets yields sentence embeddings that achieve a new state-of-the-art for the SentEval toolkit 
Ablation Study
We have demonstrated strong empirical results for the quality of SBERT sentence embeddings  In this section, we perform an ablation study of different aspects of SBERT in order to get a better understanding of their relative importance 
We evaluated different pooling strategies (MEAN, MAX, and CLS)  For the classification objective function, we evaluate different concatenation methods  For each possible configuration, we train SBERT with 10 different random seeds and average the performances 
The objective function (classification vs  regression) depends on the annotated dataset  For the classification objective function, we train SBERT-base on the SNLI and the Multi-NLI dataset  For the regression objective function, we train on the training set of the STS benchmark dataset  Performances are measured on the development split of the STS benchmark dataset  Results are shown in Table TABREF23 
When trained with the classification objective function on NLI data, the pooling strategy has a rather minor impact  The impact of the concatenation mode is much larger  InferSent BIBREF4 and Universal Sentence Encoder BIBREF5 both use $(u, v, |u-v|, u*v)$ as input for a softmax classifier  However, in our architecture, adding the element-wise $u*v$ decreased the performance 
The most important component is the element-wise difference $|u-v|$  Note, that the concatenation mode is only relevant for training the softmax classifier  At inference, when predicting similarities for the STS benchmark dataset, only the sentence embeddings $u$ and $v$ are used in combination with cosine-similarity  The element-wise difference measures the distance between the dimensions of the two sentence embeddings, ensuring that similar pairs are closer and dissimilar pairs are further apart 
When trained with the regression objective function, we observe that the pooling strategy has a large impact  There, the MAX strategy perform significantly worse than MEAN or CLS-token strategy  This is in contrast to BIBREF4, who found it beneficial for the BiLSTM-layer of InferSent to use MAX instead of MEAN pooling 
Computational Efficiency
Sentence embeddings need potentially be computed for Millions of sentences, hence, a high computation speed is desired  In this section, we compare SBERT to average GloVe embeddings, InferSent BIBREF4, and Universal Sentence Encoder BIBREF5 
For our comparison we use the sentences from the STS benchmark BIBREF10  We compute average GloVe embeddings using a simple for-loop with python dictionary lookups and NumPy  InferSent is based on PyTorch  For Universal Sentence Encoder, we use the TensorFlow Hub version, which is based on TensorFlow  SBERT is based on PyTorch  For improved computation of sentence embeddings, we implemented a smart batching strategy: Sentences with similar lengths are grouped together and are only padded to the longest element in a mini-batch  This drastically reduces computational overhead from padding tokens 
Performances were measured on a server with Intel i7-5820K CPU @ 3 30GHz, Nvidia Tesla V100 GPU, CUDA 9 2 and cuDNN  The results are depicted in Table TABREF26 
On CPU, InferSent is about 65% faster than SBERT  This is due to the much simpler network architecture  InferSent uses a single BiLSTM layer, while BERT uses 12 stacked transformer layers  However, an advantage of transformer networks is the computational efficiency on GPUs  There, SBERT with smart batching is about 9% faster than InferSent and about 55% faster than Universal Sentence Encoder  Smart batching achieves a speed-up of 89% on CPU and 48% on GPU  Average GloVe embeddings is obviously by a large margin the fastest method to compute sentence embeddings 
Conclusion
We showed that BERT out-of-the-box maps sentences to a vector space that is rather unsuitable to be used with common similarity measures like cosine-similarity  The performance for seven STS tasks was below the performance of average GloVe embeddings 
To overcome this shortcoming, we presented Sentence-BERT (SBERT)  SBERT fine-tunes BERT in a siamese / triplet network architecture  We evaluated the quality on various common benchmarks, where it could achieve a significant improvement over state-of-the-art sentence embeddings methods  Replacing BERT with RoBERTa did not yield a significant improvement in our experiments 
SBERT is computationally efficient  On a GPU, it is about 9% faster than InferSent and about 55% faster than Universal Sentence Encoder  SBERT can be used for tasks which are computationally not feasible to be modeled with BERT  For example, clustering of 10,000 sentences with hierarchical clustering requires with BERT about 65 hours, as around 50 Million sentence combinations must be computed  With SBERT, we were able to reduce the effort to about 5 seconds 
Acknowledgments
This work has been supported by the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1 and grant GU 798/17-1)  It has been co-funded by the German Federal Ministry of Education and Research (BMBF) under the promotional references 03VP02540 (ArgumenText) 
Context 2: Winograd Schemas
A Winograd schema (Levesque, Davis, and Morgenstern 2012) is a pair of sentences, or of short texts, called the elements of the schema, that satisfy the following constraints:
The following is an example of a Winograd schema:
Here, the two sentences differ only in the last word: `large' vs  `small'  The ambiguous pronoun is `it'  The two antecedents are `trophy' and `brown suitcase'  A human reader will naturally interpret `it' as referring to the trophy in the first sentence and to the suitcase in the second sentence, using the world knowledge that a small object can fit in a large container, but a large object cannot fit in a small container (Davis 2013)  Condition 4 is satisfied because either a trophy or a suitcase can be either large or small, and there is no reason to suppose that there would be a meaningful correlation of `small' or `large' with `trophy' or `suitcase' in a typical corpus 
We will say that an element of a Winograd schema is “solved” if the referent of the pronoun is identified 
An example of a pair of sentences satisfying conditions 1-3 but not 4 would be
Since women cannot be carcinogenic and pills cannot be pregnant, the pronoun `they' in these sentences is easily disambiguated using selectional restrictions  This pair is therefore not a valid Winograd schema 
The Winograd Schema Challenge (WSC) is a challenge for AI programs  The program is presented with a collection of sentences, each of which is one element of a Winograd schema, and is required to find the correct referent for the ambiguous pronoun  An AI program passes the challenge if its success rate is comparable to a human reader  The challenge is administered by commonsensereasoning org and sponsored by Nuance Inc  It was offered for the first time at IJCAI-2016 (Morgenstern, Davis, and Ortiz, in preparation); the organizers plan to continue to offer it roughly once a year 
Winograd schemas as the basis for challenges for machine translation programs
In many cases, the identification of the referent of the prounoun in a Winograd schema is critical for finding the correct translation of that pronoun in a different language  Therefore, Winograd schemas can be used as a very difficult challenge for the depth of understanding achieved by a machine translation program 
The third person plural pronoun `they' has no gender in English and most other languages (unlike the third person singular pronouns `he', `she', and `it')  However Romance languages such as French, Italian, and Spanish, and Semitic languages such as Hebrew and Arabic distinguish between the masculine and the feminine third-person plural pronouns, at least in some grammatical cases  For instance in French, the masculine pronoun is `ils'; the feminine pronoun is `elles'  In all of these cases, the masculine pronoun is standardly used for groups of mixed or unknown gender 
In order to correctly translate a sentence in English containing the word `they' into one of these languages, it is necessary to determine whether or not the referent is a group of females  If it is, then the translation must be the feminine pronoun; otherwise, it must be the masculine pronoun  Therefore, if one can create a Winograd schema in English where the ambiguous pronoun is `they' and the correct referent for one element is a collection of men and for the other is a collection of women, then to translate both elements correctly requires solving the Winograd schema 
As an example, consider the Winograd schema:
If these sentences are translated into French, then `they' in the first sentence should be translated `elles', as referring to Jane and Susan, and `they' in the second sentence should be translated `ils', as referring to Fred and George 
A number of the Winograd schemas already published can be “converted” quite easily to this form  Indeed, the above example was constructed in this way; the original form was “Jane knocked on Susan's door, but she did not [answer/get an answer] ” Of the 144 schemas in the collection at http://www cs nyu edu/faculty/davise/papers/WinogradSchemas/WSCollection html there are 33 that can plausibly be translated this way  (#'s 3, 4, 12, 15, 17, 18, 23, 25, 26, 27, 42, 43, 44, 45, 46, 57, 58, 60, 69, 70, 71, 77, 78, 79, 86, 97, 98, 106, 111, 114, 127, 132, 144) — essentially any schema that involves two people as possible referents where the content of the sentences makes sense as applied to groups of people rather than individuals 
A similar device, in the opposite direction, relies on the fact that French does not distinguish between the possessive pronouns `his' and `her'  The pronouns `son' and `sa' are gendered, but the gender agrees with the possession, not the possessor  Therefore a Winograd schema that relies on finding a referent for a possessive pronoun can be turned into a hard pair of French-to-English translation problems by making one possible referent male and the other one female  For example, schema #124 in the online collection reads “The man lifted the boy onto his [bunk bed/shoulders] ” Changing the boy to a girl and translating into French gives “L'homme leva la fille sur [ses épaules/son lit superposé]”  In the first sentence “ses” is translated “his”, in the second “son” is translated “her”  The eleven Winograd schemas #'s 112, 117, 118, 119, 124, 125, 127, 129, 130, 131, and 143 can be converted this way 
Care must be taken to avoid relying on, or seeming to rely on, objectionable stereotypes about men and women  One mechanism that can sometimes be used is to include a potentially problematic sentence in both directions  For instance, schema 23 from the WSC collection can be translated into both “The girls were bullying the boys so we [punished/rescued] them” and “The boys were bullying the girls, so we [punished/rescued] them,” thus avoiding any presupposition of whether girls are more likely to bully boys or vice versa 
A historical note: Winograd schemas were named after Terry Winograd because of a well-known example in his doctoral thesis (Winograd 1970)  In his thesis, this example followed the above form, and the importance of the example was justified in terms of machine translation  Winograd's original schema was: “The city councilmen refused to give the women a permit for a demonstration because they [feared/advocated] violence", and Winograd explained that, in the sentence with `feared', `they' would refer to the councilmen and would be translated as `ils' in French, whereas in the sentence with `advocated', `they' would refer to the women and would be translated as `elles'  In the later versions of the thesis, published as (Winograd 1972), he changed `women' to `demonstrators'; this made the disambiguation clearer, but lost the point about translation 
Current state of the art
No one familiar with the state of the art in machine translation technology or the state of the art of artificial intelligence generally will be surprised to learn that currently machine translation program are unable to solve these Winograd schema challenge problems 
What may be more surprising is that currently (July 2016), machine translation programs are unable to choose the feminine plural pronoun even when the only possible antecedent is a group of women  For instance, the sentence “The girls sang a song and they danced” is translated into French as “Les filles ont chanté une chanson et ils ont dansé” by Google Translate (GT), Bing Translate, and Yandex  In fact, I have been unable to construct any sentence in English that is translated into any language using the feminine plural pronun  Note that, since the masculine plural pronoun is used for groups of mixed gender in all these languages, it is almost certainly more common in text than the feminine plural; hence this strategy is reasonable faute de mieux  (It also seems likely that the erroneous use of a feminine plural for a masculine antecedent sounds even more jarring than the reverse )
The same thing sometimes occurs in translating the feminine plural pronoun between languages that have it  GT translates the French word `elles' into Spanish as `ellos' (the masculine form)  Curiously, in the opposite direction, it gets the right answer; the Spanish `ellas' (fem ) is translated into French as `elles'

ground_truth: GloVe, BERT, Universal Sentence Encoder, TF-IDF, InferSent

Avg. GloVe embeddings, Avg. fast-text embeddings, Avg. BERT embeddings, BERT CLS-vector, InferSent - GloVe and Universal Sentence Encoder.

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 language do they explore?
Context 2: 
Language-specific issues
The masculine and feminine plural pronouns are distinguished in the Romance languages (French, Spanish, Italian, Portuguese etc ) and in Semitic languages (Arabic, Hebrew, etc ) I have consulted with native speakers and experts in these languages about the degree to which the gender distinction is observed in practice  The experts say that in French, Spanish, Italian, and Portuguese, the distinction is very strictly observed; the use of a masculine pronoun for a feminine antecedent is jarringly wrong to a native or fluent speaker  “Les filles ont chanté une chanson et ils ont dansé” sounds as wrong to a French speaker as “The girl sang a song and he danced” sounds to an English speaker; in both cases, the hearer will interpret the pronoun as referrinig to some other persons or person, who is male  In Hebrew and Arabic, this is much less true; in speech, and even, increasingly, in writing, the masculine pronoun is often used for a feminine antecedent 
Looking further ahead, it is certainly possible that gender distinctions will be abandoned in the Romance languages, or even that English will have driven all other languages out of existence, sooner than AI systems will be able to do pronoun resolution in Winograd schemas; at that point, this test will no longer be useful 
In some cases, a translation program can side-step the issue by omitting the pronoun altogether  For example, GT translates the above sentence “The girls sang a song and they danced” into Spanish as “Las chicas cantaron una canción y bailaban” and into Italian as “Le ragazze hanno cantato una canzone e ballavano ” However, with the more complex sentences of the Winograd Schemas, this strategy will rarely give a plausible translation for both elements of the schema 
Other languages, other ambiguities
Broadly speaking, whenever a target language INLINEFORM0 requires some distinction that is optional or non-existent in source language INLINEFORM1 , it is possible to create a sentence INLINEFORM2 in INLINEFORM3 where the missing information is not explicit but can be inferred from background knowledge  Translating INLINEFORM4 from INLINEFORM5 to INLINEFORM6 thus requires using the background knowledge to resolve the ambiguity, and will therefore be challenging for automatic machine translation 
A couple of examples: The word `sie' in German serves as both the formal second person prounoun (always capitalized), the third person feminine singular, and the third person plural  Therefore, it can be translated into English as either “you”, “she”, “it”, or “they”; and into French as either `vous', `il', `elle', `ils', or `elles'  (The feminine third-person singular German `sie' can be translated as neuter in English and as masculine in French because the three languages do not slice up the worlds into genders in the same way ) Likewise, the possessive pronoun `ihr' in all its declensions can mean either `her' or `their'  In some cases, the disambiguation can be carried out on purely syntactic ground; e g  if `sie' is the subject of a third-person singular verb, it must mean `she'  However, in many case, the disambiguation requires a deeper level of understanding  Thus, it should be possible to construct German Winograd schemas based on the words `sie' or `ihr' that have to be solved in order to translate them into English  For example,
Marie fand fünf verlassene Kätzchen im Keller  Ihre Mutter war gestorben 
(Marie found five abandoned kittens in the cellar  Their mother was dead )
vs 
Marie fand fünf verlassene Kätzchen im Keller  Ihre Mutter war unzufrieden 
(Marie found five abandoned kittens in the cellar  Her mother was displeased )
Another example: French distinguishes between a male friend `ami' and a female friend `amie'  Therefore, in translating the word “friend” into French, it is necessary, if possible, to determine the sex of the friend; and the clue for that can involve an inference that, as far as AI programs are concerned, is quite remote  Human readers are awfully good at picking up on those, of course  In general, with this kind of thing, if you want to break GT or another translation program, the trick is to place a large separation between the evidence and the word being disambiguated  In this case, at the present time, the separation can be pretty small  GT correctly translates “my friend Pierre” and “my friend Marie” as “mon ami Pierre” and “mon amie Marie” and it translates “She is my friend” as “Elle est mon amie”, but rather surpringly it breaks down at “Marie is my friend,” which it translates “Marie est mon ami ” As for something like “Jacques said to Marie, `You have always been a true friend,' ” that is quite hopeless  GT can surprise one, though, in both directions; sometimes it misses a very close clue, as in “Marie is my friend”, but other times it can carry a clue further than one would have guessed 
Acknowledgements
Thanks to Arianna Bisazza, Gerhard Brewka, Antoine Cerfon, Joseph Davis, Gigi Dopico-Black, Nizar Habash, Leora Morgenstern, Oded Regev, Francesca Rossi, Vesna Sabljakovic-Fritz, and Manuela Veloso for help with the various languages and discussion of gendered pronouns 
References
E  Davis, “Qualitative Spatial Reasoning in Interpreting Text and Narrative” Spatial Cognition and Computation, 13:4, 2013, 264-294 
H  Levesque, E  Davis, and L  Morgenstern, “The Winograd Schema Challenge,” KR 2012 
L  Morgenstern, E  Davis, and C  Ortiz, “Report on the Winograd Schema Challenge, 2016”, in preparation 
T  Winograd, “Procedures as a Representation for Data in a Computer Program for Understanding Natural Language," Ph D  thesis, Department of Mathematics, MIT, August 1970  Published as MIT AITR-235, January 1971 
T  Winograd, Understanding Natural Language, Academic Press, 1972 
Context 3: Introduction
Over the past two decades, the emergence of social media has enabled the proliferation of traceable human behavior  The content posted by users can reflect who their friends are, what topics they are interested in, or which company they are working for  At the same time, users are listing a number of profile fields to define themselves to others  The utilization of such metadata has proven important in facilitating further developments of applications in advertising BIBREF0 , personalization BIBREF1 , and recommender systems BIBREF2   However, profile information can be limited, depending on the platform, or it is often deliberately omitted BIBREF3   To uncloak this information, a number of studies have utilized social media users' footprints to approximate their profiles 
This paper explores the potential of predicting a user's industry –the aggregate of enterprises in a particular field– by identifying industry indicative text in social media  The accurate prediction of users' industry can have a big impact on targeted advertising by minimizing wasted advertising BIBREF4 and improved personalized user experience  A number of studies in the social sciences have associated language use with social factors such as occupation, social class, education, and income BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8   An additional goal of this paper is to examine such findings, and in particular the link between language and occupational class, through a data-driven approach 
In addition, we explore how meaning changes depending on the occupational context  By leveraging word embeddings, we seek to quantify how, for example, cloud might mean a separate concept (e g , condensed water vapor) in the text written by users that work in environmental jobs while it might be used differently by users in technology occupations (e g , Internet-based computing) 
Specifically, this paper makes four main contributions  First, we build a large, industry-annotated dataset that contains over 20,000 blog users  In addition to their posted text, we also link a number of user metadata including their gender, location, occupation, introduction and interests 
Second, we build content-based classifiers for the industry prediction task and study the effect of incorporating textual features from the users' profile metadata using various meta-classification techniques, significantly improving both the overall accuracy and the average per industry accuracy 
Next, after examining which words are indicative for each industry, we build vector-space representations of word meanings and calculate one deviation for each industry, illustrating how meaning is differentiated based on the users' industries  We qualitatively examine the resulting industry-informed semantic representations of words by listing the words per industry that are most similar to job related and general interest terms 
Finally, we rank the different industries based on the normalized relative frequencies of emotionally charged words (positive and negative) and, in addition, discover that, for both genders, these frequencies do not statistically significantly correlate with an industry's gender dominance ratio 
After discussing related work in Section SECREF2 , we present the dataset used in this study in Section SECREF3   In Section SECREF4 we evaluate two feature selection methods and examine the industry inference problem using the text of the users' postings  We then augment our content-based classifier by building an ensemble that incorporates several metadata classifiers  We list the most industry indicative words and expose how each industrial semantic field varies with respect to a variety of terms in Section SECREF5   We explore how the frequencies of emotionally charged words in each gender correlate with the industries and their respective gender dominance ratio and, finally, conclude in Section SECREF6  
Related Work
Alongside the wide adoption of social media by the public, researchers have been leveraging the newly available data to create and refine models of users' behavior and profiling  There exists a myriad research that analyzes language in order to profile social media users  Some studies sought to characterize users' personality BIBREF9 , BIBREF10 , while others sequenced the expressed emotions BIBREF11 , studied mental disorders BIBREF12 , and the progression of health conditions BIBREF13   At the same time, a number of researchers sought to predict the social media users' age and/or gender BIBREF14 , BIBREF15 , BIBREF16 , while others targeted and analyzed the ethnicity, nationality, and race of the users BIBREF17 , BIBREF18 , BIBREF19   One of the profile fields that has drawn a great deal of attention is the location of a user  Among others, Hecht et al  Hecht11 predicted Twitter users' locations using machine learning on nationwide and state levels  Later, Han et al  Han14 identified location indicative words to predict the location of Twitter users down to the city level 
As a separate line of research, a number of studies have focused on discovering the political orientation of users BIBREF15 , BIBREF20 , BIBREF21   Finally, Li et al  Li14a proposed a way to model major life events such as getting married, moving to a new place, or graduating  In a subsequent study, BIBREF22 described a weakly supervised information extraction method that was used in conjunction with social network information to identify the name of a user's spouse, the college they attended, and the company where they are employed 
The line of work that is most closely related to our research is the one concerned with understanding the relation between people's language and their industry  Previous research from the fields of psychology and economics have explored the potential for predicting one's occupation from their ability to use math and verbal symbols BIBREF23 and the relationship between job-types and demographics BIBREF24   More recently, Huang et al  Huang15 used machine learning to classify Sina Weibo users to twelve different platform-defined occupational classes highlighting the effect of homophily in user interactions  This work examined only users that have been verified by the Sina Weibo platform, introducing a potential bias in the resulting dataset  Finally, Preotiuc-Pietro et al  Preoctiuc15 predicted the occupational class of Twitter users using the Standard Occupational Classification (SOC) system, which groups the different jobs based on skill requirements  In that work, the data collection process was limited to only users that specifically mentioned their occupation in their self-description in a way that could be directly mapped to a SOC occupational class  The mapping between a substring of their self-description and a SOC occupational class was done manually  Because of the manual annotation step, their method was not scalable; moreover, because they identified the occupation class inside a user self-description, only a very small fraction of the Twitter users could be included (in their case, 5,191 users) 
Both of these recent studies are based on micro-blogging platforms, which inherently restrict the number of characters that a post can have, and consequently the way that users can express themselves 
Moreover, both studies used off-the-shelf occupational taxonomies (rather than self-declared occupation categories), resulting in classes that are either too generic (e g , media, welfare and electronic are three of the twelve Sina Weibo categories), or too intermixed (e g , an assistant accountant is in a different class from an accountant in SOC)  To address these limitations, we investigate the industry prediction task in a large blog corpus consisting of over 20K American users, 40K web-blogs, and 560K blog posts 
Dataset
We compile our industry-annotated dataset by identifying blogger profiles located in the U S  on the profile finder on http://www blogger com, and scraping only those users that had the industry profile element completed 
For each of these bloggers, we retrieve all their blogs, and for each of these blogs we download the 21 most recent blog postings  We then clean these blog posts of HTML tags and tokenize them, and drop those bloggers whose cumulative textual content in their posts is less than 600 characters  Following these guidelines, we identified all the U S  bloggers with completed industry information 
Traditionally, standardized industry taxonomies organize economic activities into groups based on similar production processes, products or services, delivery systems or behavior in financial markets  Following such assumptions and regardless of their many similarities, a tomato farmer would be categorized into a distinct industry from a tobacco farmer  As demonstrated in Preotiuc-Pietro et al  Preoctiuc15 such groupings can cause unwarranted misclassifications 
The Blogger platform provides a total of 39 different industry options  Even though a completed industry value is an implicit text annotation, we acknowledge the same problem noted in previous studies: some categories are too broad, while others are very similar  To remedy this and following Guibert et al  Guibert71, who argued that the denominations used in a classification must reflect the purpose of the study, we group the different Blogger industries based on similar educational background and similar technical terminology  To do that, we exclude very general categories and merge conceptually similar ones  Examples of broad categories are the Education and the Student options: a teacher could be teaching in any concentration, while a student could be enrolled in any discipline  Examples of conceptually similar categories are the Investment Banking and the Banking options 
The final set of categories is shown in Table TABREF1 , along with the number of users in each category  The resulting dataset consists of 22,880 users, 41,094 blogs, and 561,003 posts  Table TABREF2 presents additional statistics of our dataset 
Text-based Industry Modeling
After collecting our dataset, we split it into three sets: a train set, a development set, and a test set  The sizes of these sets are 17,880, 2,500, and 2,500 users, respectively, with users randomly assigned to these sets  In all the experiments that follow, we evaluate our classifiers by training them on the train set, configure the parameters and measure performance on the development set, and finally report the prediction accuracy and results on the test set  Note that all the experiments are performed at user level, i e , all the data for one user is compiled into one instance in our data sets 
To measure the performance of our classifiers, we use the prediction accuracy  However, as shown in Table TABREF1 , the available data is skewed across categories, which could lead to somewhat distorted accuracy numbers depending on how well a model learns to predict the most populous classes  Moreover, accuracy alone does not provide a great deal of insight into the individual performance per industry, which is one of the main objectives in this study  Therefore, in our results below, we report: (1) micro-accuracy ( INLINEFORM0 ), calculated as the percentage of correctly classified instances out of all the instances in the development (test) data; and (2) macro-accuracy ( INLINEFORM1 ), calculated as the average of the per-category accuracies, where the per-category accuracy is the percentage of correctly classified instances out of the instances belonging to one category in the development (test) data 
Leveraging Blog Content
In this section, we seek the effectiveness of using solely textual features obtained from the users' postings to predict their industry 
The industry prediction baseline Majority is set by discovering the most frequently featured class in our training set and picking that class in all predictions in the respective development or testing set 
After excluding all the words that are not used by at least three separate users in our training set, we build our AllWords model by counting the frequencies of all the remaining words and training a multinomial Naive Bayes classifier  As seen in Figure FIGREF3 , we can far exceed the Majority baseline performance by incorporating basic language signals into machine learning algorithms (173% INLINEFORM0 improvement) 
We additionally explore the potential of improving our text classification task by applying a number of feature ranking methods and selecting varying proportions of top ranked features in an attempt to exclude noisy features  We start by ranking the different features, w, according to their Information Gain Ratio score (IGR) with respect to every industry, i, and training our classifier using different proportions of the top features  INLINEFORM0 INLINEFORM1
Even though we find that using the top 95% of all the features already exceeds the performance of the All Words model on the development data, we further experiment with ranking our features with a more aggressive formula that heavily promotes the features that are tightly associated with any industry category  Therefore, for every word in our training set, we define our newly introduced ranking method, the Aggressive Feature Ranking (AFR), as: INLINEFORM0
In Figure FIGREF3 we illustrate the performance of all four methods in our industry prediction task on the development data  Note that for each method, we provide both the accuracy ( INLINEFORM0 ) and the average per-class accuracy ( INLINEFORM1 )  The Majority and All Words methods apply to all the features; therefore, they are represented as a straight line in the figure  The IGR and AFR methods are applied to varying subsets of the features using a 5% step 
Our experiments demonstrate that the word choice that the users make in their posts correlates with their industry  The first observation in Figure FIGREF3 is that the INLINEFORM0 is proportional to INLINEFORM1 ; as INLINEFORM2 increases, so does INLINEFORM3   Secondly, the best result on the development set is achieved by using the top 90% of the features using the AFR method  Lastly, the improvements of the IGR and AFR feature selections are not substantially better in comparison to All Words (at most 5% improvement between All Words and AFR), which suggest that only a few noisy features exist and most of the words play some role in shaping the “language" of an industry 
As a final evaluation, we apply on the test data the classifier found to work best on the development data (AFR feature selection, top 90% features), for an INLINEFORM0 of 0 534 and INLINEFORM1 of 0 477 
Leveraging User Metadata
Together with the industry information and the most recent postings of each blogger, we also download a number of accompanying profile elements  Using these additional elements, we explore the potential of incorporating users' metadata in our classifiers 
Table TABREF7 shows the different user metadata we consider together with their coverage percentage (not all users provide a value for all of the profile elements)  With the exception of the gender field, the remaining metadata elements shown in Table TABREF7 are completed by the users as a freely editable text field  This introduces a considerable amount of noise in the set of possible metadata values  Examples of noise in the occupation field include values such as “Retired”, “I work ”, or “momma” which are not necessarily informative for our industry prediction task 
To examine whether the metadata fields can help in the prediction of a user's industry, we build classifiers using the different metadata elements  For each metadata element that has a textual value, we use all the words in the training set for that field as features  The only two exceptions are the state field, which is encoded as one feature that can take one out of 50 different values representing the 50 U S  states; and the gender field, which is encoded as a feature with a distinct value for each user gender option: undefined, male, or female 
As shown in Table TABREF9 , we build four different classifiers using the multinomial NB algorithm: Occu (which uses the words found in the occupation profile element), Intro (introduction), Inter (interests), and Gloc (combined gender, city, state) 
In general, all the metadata classifiers perform better than our majority baseline ( INLINEFORM0 of 18 88%)  For the Gloc classifier, this result is in alignment with previous studies BIBREF24   However, the only metadata classifier that outperforms the content classifier is the Occu classifier, which despite missing and noisy occupation values exceeds the content classifier's performance by an absolute 3 2% 
To investigate the promise of combining the five different classifiers we have built so far, we calculate their inter-prediction agreement using Fleiss's Kappa BIBREF25 , as well as the lower prediction bounds using the double fault measure BIBREF26   The Kappa values, presented in the lower left side of Table TABREF10 , express the classification agreement for categorical items, in this case the users' industry  Lower values, especially values below 30%, mean smaller agreement  Since all five classifiers have better-than-baseline accuracy, this low agreement suggests that their predictions could potentially be combined to achieve a better accumulated result 
Moreover, the double fault measure values, which are presented in the top-right hand side of Table TABREF10 , express the proportion of test cases for which both of the two respective classifiers make false predictions, essentially providing the lowest error bound for the pairwise ensemble classifier performance  The lower those numbers are, the greater the accuracy potential of any meta-classification scheme that combines those classifiers  Once again, the low double fault measure values suggest potential gain from a combination of the base classifiers into an ensemble of models 
After establishing the promise of creating an ensemble of classifiers, we implement two meta-classification approaches  First, we combine our classifiers using features concatenation (or early fusion)  Starting with our content-based classifier (Text), we successively add the features derived from each metadata element  The results, both micro- and macro-accuracy, are presented in Table TABREF12   Even though all these four feature concatenation ensembles outperform the content-based classifier in the development set, they fail to outperform the Occu classifier 
Second, we explore the potential of using stacked generalization (or late fusion) BIBREF27   The base classifiers, referred to as L0 classifiers, are trained on different folds of the training set and used to predict the class of the remaining instances  Those predictions are then used together with the true label of the training instances to train a second classifier, referred to as the L1 classifier: this L1 is used to produce the final prediction on both the development data and the test data  Traditionally, stacking uses different machine learning algorithms on the same training data  However in our case, we use the same algorithm (multinomial NB) on heterogeneous data (i e , different types of data such as content, occupation, introduction, interests, gender, city and state) in order to exploit all available sources of information 
The ensemble learning results on the development set are shown in Table TABREF12   We notice a constant improvement for both metrics when adding more classifiers to our ensemble except for the Gloc classifier, which slightly reduces the performance  The best result is achieved using an ensemble of the Text, Occu, Intro, and Inter L0 classifiers; the respective performance on the test set is an INLINEFORM0 of 0 643 and an INLINEFORM1 of 0 564  Finally, we present in Figure FIGREF11 the prediction accuracy for the final classifier for each of the different industries in our test dataset  Evidently, some industries are easier to predict than others  For example, while the Real Estate and Religion industries achieve accuracy figures above 80%, other industries, such as the Banking industry, are predicted correctly in less than 17% of the time  Anecdotal evidence drawn from the examination of the confusion matrix does not encourage any strong association of the Banking class with any other  The misclassifications are roughly uniform across all other classes, suggesting that the users in the Banking industry use language in a non-distinguishing way 
Qualitative Analysis
In this section, we provide a qualitative analysis of the language of the different industries 
Top-Ranked Words
To conduct a qualitative exploration of which words indicate the industry of a user, Table TABREF14 shows the three top-ranking content words for the different industries using the AFR method 
Not surprisingly, the top ranked words align well with what we would intuitively expect for each industry  Even though most of these words are potentially used by many users regardless of their industry in our dataset, they are still distinguished by the AFR method because of the different frequencies of these words in the text of each industry 
Industry-specific Word Similarities
Next, we examine how the meaning of a word is shaped by the context in which it is uttered  In particular, we qualitatively investigate how the speakers' industry affects meaning by learning vector-space representations of words that take into account such contextual information  To achieve this, we apply the contextualized word embeddings proposed by Bamman et al  Bamman14, which are based on an extension of the “skip-gram" language model BIBREF28  
In addition to learning a global representation for each word, these contextualized embeddings compute one deviation from the common word embedding representation for each contextual variable, in this case, an industry option  These deviations capture the terms' meaning variations (shifts in the INLINEFORM0 -dimensional space of the representations, where INLINEFORM1 in our experiments) in the text of the different industries, however all the embeddings are in the same vector space to allow for comparisons to one another 
Using the word representations learned for each industry, we present in Table TABREF16 the terms in the Technology and the Tourism industries that have the highest cosine similarity with a job-related word, customers  Similarly, Table TABREF17 shows the words in the Environment and the Tourism industries that are closest in meaning to a general interest word, food  More examples are given in the Appendix SECREF8  
The terms that rank highest in each industry are noticeably different  For example, as seen in Table TABREF17 , while food in the Environment industry is similar to nutritionally and locally, in the Tourism industry the same word relates more to terms such as delicious and pastries  These results not only emphasize the existing differences in how people in different industries perceive certain terms, but they also demonstrate that those differences can effectively be captured in the resulting word embeddings 
Emotional Orientation per Industry and Gender
As a final analysis, we explore how words that are emotionally charged relate to different industries  To quantify the emotional orientation of a text, we use the Positive Emotion and Negative Emotion categories in the Linguistic Inquiry and Word Count (LIWC) dictionary BIBREF29   The LIWC dictionary contains lists of words that have been shown to correlate with the psychological states of people that use them; for example, the Positive Emotion category contains words such as “happy,” “pretty,” and “good ”
For the text of all the users in each industry we measure the frequencies of Positive Emotion and Negative Emotion words normalized by the text's length  Table TABREF20 presents the industries' ranking for both categories of words based on their relative frequencies in the text of each industry 
We further perform a breakdown per-gender, where we once again calculate the proportion of emotionally charged words in each industry, but separately for each gender  We find that the industry rankings of the relative frequencies INLINEFORM0 of emotionally charged words for the two genders are statistically significantly correlated, which suggests that regardless of their gender, users use positive (or negative) words with a relative frequency that correlates with their industry  (In other words, even if e g , Fashion has a larger number of women users, both men and women working in Fashion will tend to use more positive words than the corresponding gender in another industry with a larger number of men users such as Automotive )
Finally, motivated by previous findings of correlations between job satisfaction and gender dominance in the workplace BIBREF30 , we explore the relationship between the usage of Positive Emotion and Negative Emotion words and the gender dominance in an industry  Although we find that there are substantial gender imbalances in each industry (Appendix SECREF9 ), we did not find any statistically significant correlation between the gender dominance ratio in the different industries and the usage of positive (or negative) emotional words in either gender in our dataset 
Conclusion
In this paper, we examined the task of predicting a social media user's industry  We introduced an annotated dataset of over 20,000 blog users and applied a content-based classifier in conjunction with two feature selection methods for an overall accuracy of up to 0 534, which represents a large improvement over the majority class baseline of 0 188 
We also demonstrated how the user metadata can be incorporated in our classifiers  Although concatenation of features drawn both from blog content and profile elements did not yield any clear improvements over the best individual classifiers, we found that stacking improves the prediction accuracy to an overall accuracy of 0 643, as measured on our test dataset  A more in-depth analysis showed that not all industries are equally easy to predict: while industries such as Real Estate and Religion are clearly distinguishable with accuracy figures over 0 80, others such as Banking are much harder to predict 
Finally, we presented a qualitative analysis to provide some insights into the language of different industries, which highlighted differences in the top-ranked words in each industry, word semantic similarities, and the relative frequency of emotionally charged words 
Acknowledgments
This material is based in part upon work supported by the National Science Foundation (#1344257) and by the John Templeton Foundation (#48503)  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the John Templeton Foundation 
Additional Examples of Word Similarities

ground_truth: English, French, German 

French, English, Spanish, Italian, Portuguese, Hebrew, Arabic

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 many users do they look at?
Context 4:  The training set includes $221 8$ thousand sentences from 285 dialogues, with 12 and 46 conversations reserved for the development and test set 
For speech recognition experiments, we ensure that our test set includes unique speakers as well as speakers that overlap with the training set, in order to allow for comparisons of the ability of the speech recognition system to generalize over seen and new speakers  For consistency, we use the same dataset splits for the machine translation experiments  The statistics in Table reflect this split 
Applications
Our resource has the potential to be the basis of computational research in Mapudungun across several areas  Since the collected audio has been transcribed, our resource is appropriate for the study of automatic speech recognition and speech synthesis  The Spanish translations enable the creation of machine translation systems between Mapudungun and Spanish, as well as end-to-end (or direct) speech translation  We in fact built such speech synthesis, speech recognition, and machine translation systems as a showcase of the usefulness of our corpus in that research direction 
Furthermore, our annotations of the Spanish words interspersed in Mapudungun speech could allow for a study of code-switching patterns within the Mapuche community  In addition, our annotations of non-standardized orthographic transcriptions could be extremely useful in the study of historical language and orthography change as a language moves from predominantly oral to being written in a standardized orthography, as well as in building spelling normalization and correction systems  The relatively large amount of data that we collected will also allow for the training of large language models, which in turn could be used as the basis for predictive keyboards tailored to Mapudungun  Last, since all data are dialogues annotated for the different speaker turns, they could be useful for building Mapudungun dialogue systems and chatbot-like applications 
The potential applications of our resource, however, are not exhausted in language technologies  The resource as a whole could be invaluable for ethnographic and sociological research, as the conversations contrast traditional and Western medicine practices, and they could reveal interesting aspects of the Mapuche culture 
In addition, the corpus is a goldmine of data for studying the morphostyntax of Mapudungun BIBREF8  As an isolate polysynthetic language, the study of Mapudungun can provide insights into the range of possibilities within human languages can work 
Baseline Results
Using the aforementioned higher quality portions of the corpus, we trained baseline systems for Mapudungun speech recognition and speech synthesis, as well as Machine Translation systems between Mapudungun and Spanish 
Baseline Results ::: Speech Synthesis
In our previous work on building speech systems on found data in 700 languages, BIBREF7, we addressed alignment issues (when audio is not segmented into turn/sentence sized chunks) and correctness issues (when the audio does not match the transcription)  We used the same techniques here, as described above 
For the best quality speech synthesis we need a few hours of phonetically-balanced, single-speaker, read speech  Our first step was to use the start and end points for each turn in the dialogues, and select those of the most frequent speaker, nmlch  This gave us around 18250 segments  We further automatically removed excessive silence from the start, middle and end of these turns (based on occurrence of F0)  This gave us 13 hours and 48 minutes of speech 
We phonetically aligned this data and built a speech clustergen statistical speech synthesizer BIBREF9 from all of this data  We resynthesized all of the data and measured the difference between the synthesized data and the original data using Mel Cepstral Distortion, a standard method for automatically measuring quality of speech generation BIBREF10  We then ordered the segments by their generation score and took the top 2000 turns to build a new synthesizer, assuming the better scores corresponded to better alignments, following the techniques of BIBREF7 
The initial build gave an MCD on held out data of 6 483  While the 2000 best segment dataset gives an MCD of 5 551, which is a large improvement  The quality of the generated speech goes from understandable, only if you can see the text, to understandable, and transcribable even for non-Mapudungun speakers  We do not believe we are building the best synthesizer with our current (non-neural) techniques, but we do believe we are selecting the best training data for other statistical and neural training techniques in both speech synthesis and speech recognition 
Baseline Results ::: Speech Recognition
For speech recognition (ASR) we used Kaldi BIBREF11  As we do not have access to pronunciation lexica for Mapudungun, we had to approximate them with two settings  In the first setting, we make the simple assumption that each character corresponds to a pronunced phoneme  In the second setting, we instead used the generated phonetic lexicon also used in the above-mentioned speech synthesis techniques  The train/dev/test splits are across conversations, as described above 
Under the first setting, we obtained a 60% character error rate, while the generated lexicon significantly boosts performance, as our systems achieve a notably reduced 30% phone error rate  Naturally, these results are relatively far from the quality of ASR systems trained on large amounts of clean data such as those available in English  Given the quality of the recordings, and the lack of additional resources, we consider our results fairly reasonable and they would still be usable for simple dialog-like tasks  We anticipate, though, that one could significantly improve ASR quality over our dataset, by using in-domain language models, or by training end-to-end neural recognizers leveraging languages with similar phonetic inventories BIBREF12 or by using the available Spanish translations in a multi-source scenario BIBREF13 
Baseline Results ::: Mapudungun–Spanish Machine Translation
We built neural end-to-end machine translation systems between Mapudungun and Spanish in both directions, using state-of-the-art Transformer architecture BIBREF14 with the toolkit of BIBREF15  We train our systems at the subword level using Byte-Pair Encoding BIBREF16 with a vocabulary of 5000 subwords, shared between the source and target languages  We use five layers for each of the encoder and the decoder, an embedding size of 512, feed forward transformation size of 2048, and eight attention heads  We use dropout BIBREF17 with $0 4$ probability as well as label smoothing set to $0 1$  We train with the Adam optimizer BIBREF18 for up to 200 epochs using learning decay with a patience of six epochs 
The baseline results using different portions of the training set (10k, 50k, 100k, and all (220k) parallel sentences) on both translation directions are presented in Table , using detokenized BLEU BIBREF19 (a standard MT metric) and chrF BIBREF20 (a metric that we consider to be more appropriate for polysynthetic languages, as it does not rely on word n-grams) computed with the sacreBLEU toolkit BIBREF21  It it worth noting the difference in quality between the two directions, with translation into Spanish reaching 20 4 (almost 21) BLEU points in the development set, while the opposite direction (translating into Mapudungun) shows about a 7 BLEU points worse performance  This is most likely due to Mapudungun being a polysynthetic language, with its complicated morphology posing a challenge for proper generation 
Related Work
Mapudungun grammar has been studied since the arrival of European missionaries and colonizers hundreds of years ago  More recent descriptions of Mapudungun grammar BIBREF1 and BIBREF0 informed the collection of the resource that we are presenting in this paper 
Portions of our resource have been used in early efforts to build language systems for Mapudungun  In particular, BIBREF22 focused on Mapudungun morphology in order to create spelling correction systems, while BIBREF23, BIBREF6, BIBREF24, and BIBREF25 developed hybrid rule- and phrase-based Statistical Machine Translation systems 
Naturally, similar works in collecting corpora in Indigenous languages of Latin America are abundant, but very few, if any, have the scale and potential of our resource to be useful in many downstream language-specific and inter-disciplinary applications  A general overview of the state of NLP for the under-represented languages of the Americas can be found at BIBREF26  To name a few of the many notable works, BIBREF27 created a parallel Mixtec-Spanish corpus for Machine Translation and BIBREF28 created lexical resources for Arapaho, while BIBREF29 and BIBREF30 focused on building speech corpora for Southern Quechua and Chatino respectively 
Conclusion
With this work we present a resource that will be extremely useful for building language systems in an endangered, under-represented language, Mapudungun  We benchmark NLP systems for speech synthesis, speech recognition, and machine translation, providing strong baseline results  The size of our resource (142 hours, more than 260k total sentences) has the potential to alleviate many of the issues faced when building language technologies for Mapudungun, in contrast to other indigenous languages of the Americas that unfortunately remain low-resource 
Our resource could also be used for ethnographic and anthropological research into the Mapuche culture, and has the potential to contribute to intercultural bilingual education, preservation activities and further general advancement of the Mapudungun-speaking community 
Acknowledgements
The data collection described in this paper was supported by NSF grants IIS-0121631 (AVENUE) and IIS-0534217 (LETRAS), with supplemental funding from NSF's Office of International Science and Education  Preliminary funding for work on Mapudungun was also provided by DARPA The experimental material is based upon work generously supported by the National Science Foundation under grant 1761548 

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