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. Is WordNet useful for taxonomic reasoning for this task?
Context 1: Introduction
Automatically answering questions, especially in the open-domain setting (i e , where minimal or no contextual knowledge is explicitly provided), requires bringing to bear considerable amount of background knowledge and reasoning abilities  For example, knowing the answers to the two questions in Figure FIGREF1 requires identifying a specific ISA relation (i e , that cooking is a type of learned behavior) as well as recalling the definition of a concept (i e , that global warming is defined as a worldwide increase in temperature)  In the multiple-choice setting, which is the variety of question-answering (QA) that we focus on in this paper, there is also pragmatic reasoning involved in selecting optimal answer choices (e g , while greenhouse effect might in some other context be a reasonable answer to the second question in Figure FIGREF1, global warming is a preferable candidate) 
Recent successes in QA, driven largely by the creation of new resources BIBREF2, BIBREF3, BIBREF4, BIBREF5 and advances in model pre-training BIBREF6, BIBREF7, raise a natural question: do state-of-the-art multiple-choice QA (MCQA) models that excel at standard tasks really have basic knowledge and reasoning skills?
Most existing MCQA datasets are constructed through either expensive crowd-sourcing BIBREF8 or hand engineering effort, in the former case making it possible to collect large amounts of data at the cost of losing systematic control over the semantics of the target questions  Hence, doing a controlled experiment to answer such a question for QA is difficult given a lack of targeted challenge datasets 
Having definitive empirical evidence of model competence on any given phenomenon requires constructing a wide range of systematic tests  For example, in measuring competence of definitions, not only do we want to see that the model can handle individual questions such as Figure FIGREF1 1 inside of benchmark tasks, but that it can answer a wider range of questions that exhaustively cover a broad set of concepts and question perturbations (i e , systematic adjustments to how the questions are constructed)  The same applies to ISA reasoning; not only is it important to recognize in the question in Figure FIGREF1 1 that cooking is a learned behavior, but also that cooking is a general type of behavior or, through a few more inferential steps, a type of human activity 
In this paper, we look at systematically constructing such tests by exploiting the vast amounts of structured information contained in various types of expert knowledge such as knowledge graphs and lexical taxonomies  Our general methodology works as illustrated in Figure FIGREF1: given any MCQA model trained on a set of benchmark tasks, we systematically generate a set of synthetic dataset probes (i e , MCQA renderings of the target information) from information in expert knowledge sources  We then use these probes to ask two empirical questions: 1) how well do models trained on benchmark tasks perform on these probing tasks and; 2) can such models be re-trained to master new challenges with minimal performance loss on their original tasks?
While our methodology is amenable to any knowledge source and set of models/benchmark tasks, we focus on probing state-of-the-art transformer models BIBREF7, BIBREF9 in the domain of science MCQA  For sources of expert knowledge, we use WordNet, a comprehensive lexical ontology, and other publicly available dictionary resources  We devise probes that measure model competence in definition and taxonomic knowledge in different settings (including hypernymy, hyponymy, and synonymy detection, and word sense disambiguation)  This choice is motivated by fact that the science domain is considered particularly challenging for QA BIBREF10, BIBREF11, BIBREF12, and existing science benchmarks are known to involve widespread use of such knowledge (see BIBREF1, BIBREF13 for analysis), which is also arguably fundamental to more complex forms of reasoning 
We show that accurately probing QA models via synthetic datasets is not straightforward, as unexpected artifacts can easily arise in such data  This motivates our carefully constructed baselines and close data inspection to ensure probe quality 
Our results confirm that transformer-based QA models have a remarkable ability to recognize certain types of knowledge captured in our probes—even without additional fine-tuning  Such models can even outperform strong task-specific models trained directly on our probing tasks (e g , on definitions, our best model achieves 77% test accuracy without specialized training, as opposed to 51% for a task-specific LSTM-based model)  We also show that the same models can be effectively re-fine-tuned on small samples (even 100 examples) of probe data, and that high performance on the probes tends to correlate with a smaller drop in the model's performance on the original QA task 
Our comprehensive assessment reveals several interesting nuances to the overall positive trend  For example, the performance of even the best QA models degrades substantially on our hyponym probes (by 8-15%) when going from 1-hop links to 2-hops  Further, the accuracy of even our best models on the WordNetQA probe drops by 14-44% under our cluster-based analysis, which assesses whether a model knows several facts about each individual concept, rather than just being good at answering isolated questions  State-of-the-art QA models thus have much room to improve even in some fundamental building blocks, namely definitions and taxonomic hierarchies, of more complex forms of reasoning 
Related Work
We follow recent work on constructing challenge datasets for probing neural models, which has primarily focused on the task of natural language inference (NLI) BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18  Most of this work looks at constructing data through adversarial generation methods, which have also been found useful for creating stronger models BIBREF19  There has also been work on using synthetic data of the type we consider in this paper BIBREF20, BIBREF21, BIBREF22  We closely follow the methodology of BIBREF22, who use hand-constructed linguistic fragments to probe NLI models and study model re-training using a variant of the inoculation by fine-tuning strategy of BIBREF23  In contrast, we focus on probing open-domain MCQA models (see BIBREF24 for a related study in the reading comprehension setting) as well as constructing data from much larger sources of structured knowledge 
Our main study focuses on probing the BERT model and fine-tuning approach of BIBREF7, and other variants thereof, which are all based on the transformer architecture of BIBREF25  Related to our efforts, there have been recent studies into the types of relational knowledge contained in large-scale knowledge models BIBREF26, BIBREF27, BIBREF28, which, similar to our work, probe models using structured knowledge sources  This prior work, however, primarily focuses on unearthing the knowledge contained in the underlying language models as is without further training, using simple (single token) cloze-style probing tasks and templates (similar to what we propose in Section SECREF3)  In contrast, we focus on understanding the knowledge contained in language models after they have been trained for a QA end-task using benchmark datasets in which such knowledge is expected to be widespread  Further, our evaluation is done before and after these models are fine-tuned on our probe QA tasks, using a more complex set of QA templates and target inferences 
The use of lexical resources and knowledge graphs such as WordNet to construct datasets has a long history, and has recently appeared in work on adversarial attacks BIBREF14, BIBREF29 and general task construction BIBREF30, BIBREF31  In the area of MCQA, there is related work on constructing questions from tuples BIBREF32, BIBREF3, both of which involve standard crowd annotation to elicit question-answer pairs (see also BIBREF33, BIBREF34)  In contrast to this work, we focus on generating data in an entirely automatic fashion, which obviates the need for expensive annotation and gives us the flexibility to construct much larger datasets that control a rich set of semantic aspects of the target questions 
Dataset Probes and Construction
Our probing methodology starts by constructing challenge datasets (Figure FIGREF1, yellow box) from a target set of knowledge resources  Each of our probing datasets consists of multiple-choice questions that include a question $\textbf {q}$ and a set of answer choices or candidates $\lbrace a_{1},   a_{N}\rbrace $  This section describes in detail the 5 different datasets we build, which are drawn from two sources of expert knowledge, namely WordNet BIBREF35 and the GNU Collaborative International Dictionary of English (GCIDE)  We describe each resource in turn, and explain how the resulting dataset probes, which we call WordNetQA and DictionaryQA, are constructed 
For convenience, we will describe each source of expert knowledge as a directed, edge-labeled graph $G$  The nodes of this graph are $\mathcal {V} = \mathcal {C} \cup \mathcal {W} \cup \mathcal {S} \cup \mathcal {D}$, where $\mathcal {C}$ is a set of atomic concepts, $\mathcal {W}$ a set of words, $\mathcal {S}$ a set of sentences, and $\mathcal {D}$ a set of definitions (see Table TABREF4 for details for WordNet and GCIDE)  Each edge of $G$ is directed from an atomic concept in $\mathcal {C}$ to another node in $V$, and is labeled with a relation, such as hypernym or isa$^\uparrow $, from a set of relations $\mathcal {R}$ (see Table TABREF4) 
When defining our probe question templates, it will be useful to view $G$ as a set of (relation, source, target) triples $\mathcal {T} \subseteq \mathcal {R} \times \mathcal {C} \times \mathcal {V}$  Due to their origin in an expert knowledge source, such triples preserve semantic consistency  For instance, when the relation in a triple is def, the corresponding edge maps a concept in $\mathcal {C}$ to a definition in $\mathcal {D}$ 
To construct probe datasets, we rely on two heuristic functions, defined below for each individual probe: $\textsc {gen}_{\mathcal {Q}}(\tau )$, which generates gold question-answer pairs $(\textbf {q},\textbf {a})$ from a set of triples $\tau \subseteq \mathcal {T}$ and question templates $\mathcal {Q}$, and $\textsc {distr}(\tau ^{\prime })$, which generates distractor answers choices $\lbrace a^{\prime }_{1},   a^{\prime }_{N-1} \rbrace $ based on another set of triples $\tau ^{\prime }$ (where usually $\tau \subset \tau ^{\prime }$)  For brevity, we will use $\textsc {gen}(\tau )$ to denote $\textsc {gen}_{\mathcal {Q}}(\tau )$, leaving question templates $\mathcal {Q}$ implicit 
Dataset Probes and Construction ::: WordNetQA
WordNet is an English lexical database consisting of around 117k concepts, which are organized into groups of synsets that each contain a gloss (i e , a definition of the target concept), a set of representative English words (called lemmas), and, in around 33k synsets, example sentences  In addition, many synsets have ISA links to other synsets that express complex taxonomic relations  Figure FIGREF6 shows an example and Table TABREF4 summarizes how we formulate WordNet as a set of triples $\mathcal {T}$ of various types  These triples together represent a directed, edge-labeled graph $G$  Our main motivation for using WordNet, as opposed to a resource such as ConceptNet BIBREF36, is the availability of glosses ($\mathcal {D}$) and example sentences ($\mathcal {S}$), which allows us to construct natural language questions that contextualize the types of concepts we want to probe 
Dataset Probes and Construction ::: WordNetQA ::: Example Generation @!START@$\textsc {gen}(\tau )$@!END@ 
We build 4 individual datasets based on semantic relations native to WordNet (see BIBREF37): hypernymy (i e , generalization or ISA reasoning up a taxonomy, ISA$^\uparrow $), hyponymy (ISA$^{\downarrow }$), synonymy, and definitions  To generate a set of questions in each case, we employ a number of rule templates $\mathcal {Q}$ that operate over tuples  A subset of such templates is shown in Table TABREF8  The templates were designed to mimic naturalistic questions we observed in our science benchmarks 
For example, suppose we wish to create a question $\textbf {q}$ about the definition of a target concept $c \in \mathcal {C}$  We first select a question template from $\mathcal {Q}$ that first introduces the concept $c$ and its lemma $l \in \mathcal {W}$ in context using the example sentence $s \in \mathcal {S}$, and then asks to identify the corresponding WordNet gloss $d \in \mathcal {D}$, which serves as the gold answer $\textbf {a}$  The same is done for ISA reasoning; each question about a hypernym/hyponym relation between two concepts $c \rightarrow ^{\uparrow /\downarrow } c^{\prime } \in \mathcal {T}_{i}$ (e g , $\texttt {dog} \rightarrow ^{\uparrow /\downarrow } \texttt {animal/terrier}$) first introduces a context for $c$ and then asks for an answer that identifies $c^{\prime }$ (which is also provided with a gloss so as to contain all available context) 
In the latter case, the rules $(\texttt {isa}^{r},c,c^{\prime }) \in \mathcal {T}_i$ in Table TABREF8 cover only direct ISA links from $c$ in direction $r \in \lbrace \uparrow ,\downarrow \rbrace $  In practice, for each $c$ and direction $r$, we construct tests that cover the set HOPS$(c,r)$ of all direct as well as derived ISA relations of $c$:
This allows us to evaluate the extent to which models are able to handle complex forms of reasoning that require several inferential steps or hops 
Dataset Probes and Construction ::: WordNetQA ::: Distractor Generation: @!START@$\textsc {distr}(\tau ^{\prime })$@!END@ 
An example of how distractors are generated is shown in Figure FIGREF6, which relies on similar principles as above  For each concept $c$, we choose 4 distractor answers that are close in the WordNet semantic space  For example, when constructing hypernymy tests for $c$ from the set hops$(c,\uparrow )$, we build distractors by drawing from $\textsc {hops}(c,\downarrow )$ (and vice versa), as well as from the $\ell $-deep sister family of $c$, defined as follows  The 1-deep sister family is simply $c$'s siblings or sisters, i e , the other children $\tilde{c} \ne c$ of the parent node $c^{\prime }$ of $c$  For $\ell > 1$, the $\ell $-deep sister family also includes all descendants of each $\tilde{c}$ up to $\ell -1$ levels deep, denoted $\textsc {hops}_{\ell -1}(\tilde{c},\downarrow )$  Formally:
For definitions and synonyms we build distractors from all of these sets (with a similar restriction on the depth of sister distractors as noted above)  In doing this, we can systematically investigate model performance on a wide range of distractor sets 
Dataset Probes and Construction ::: WordNetQA ::: Perturbations and Semantic Clusters
Based on how we generate data, for each concept $c$ (i e , atomic WordNet synset) and probe type (i e , definitions, hypernymy, etc ), we have a wide variety of questions related to $c$ that manipulate 1) the complexity of reasoning that is involved (e g , the number of inferential hops) and; 2) the types of distractors (or distractor perturbations) that are employed  We call such sets semantic clusters  As we describe in the next section, semantic clusters allow us to devise new types of evaluation that reveal whether models have comprehensive and consistent knowledge of target concepts (e g , evaluating whether a model can correctly answer several questions associated with a concept, as opposed to a few disjoint instances) 
Details of the individual datasets are shown in Table TABREF12  From these sets, we follow BIBREF22 in allocating a maximum of 3k examples for training and reserve the rest for development and testing  Since we are interested in probing, having large held-out sets allows us to do detailed analysis and cluster-based evaluation 
Dataset Probes and Construction ::: DictionaryQA
The DictionaryQA dataset is created from the GCIDE dictionary, which is a comprehensive open-source English dictionary built largely from the Webster's Revised Unabridged Dictionary BIBREF38  Each entry consists of a word, its part-of-speech, its definition, and an optional example sentence (see Table TABREF14)  Overall, 33k entries (out of a total of 155k) contain example sentences/usages  As with the WordNet probes, we focus on this subset so as to contextualize each word being probed  In contrast to WordNet, GCIDE does not have ISA relations or explicit synsets, so we take each unique entry to be a distinct sense  We then use the dictionary entries to create a probe that centers around word-sense disambiguation, as described below 
Dataset Probes and Construction ::: DictionaryQA ::: Example and Distractor Generation 
To generate gold questions and answers, we use the same generation templates for definitions exemplified in Figure TABREF8 for WordNetQA  To generate distractors, we simply take alternative definitions for the target words that represent a different word sense (e g , the alternative definitions of gift shown in Table TABREF14), as well as randomly chosen definitions if needed to create a 5-way multiple choice question  As above, we reserve a maximum of 3k examples for training  Since we have only 9k examples in total in this dataset (see WordSense in Table TABREF12), we also reserve 3k each for development and testing 
We note that initial attempts to build this dataset through standard random splitting gave rise to certain systematic biases that were exploited by the choice-only baseline models described in the next section, and hence inflated overall model scores  After several efforts at filtering we found that, among other factors, using definitions from entries without example sentences as distractors (e g , the first two entries in Table TABREF14) had a surprising correlation with such biases  This suggests that possible biases involving differences between dictionary entries with and without examples can taint the resulting automatically generated MCQA dataset (for more discussion on the pitfalls involved with automatic dataset construction, see Section SECREF5) 
Probing Methodology and Modeling
Given the probes above, we now can start to answer the empirical questions posed at the beginning  Our main focus is on looking at transformer-based MCQA models trained in the science domain (using the benchmarks shown in Table TABREF21)  In this section, we provide details of MCQA and the target models, as well as several baselines that we use to sanity check our new datasets  To evaluate model competence, we look at a combination of model performance after science pre-training and after additional model fine-tuning using the lossless inoculation strategy of BIBREF22 (Section SECREF22)  In Section SECREF24, we also discuss a cluster-level accuracy metric for measuring performance over semantic clusters 
Probing Methodology and Modeling ::: Task Definition and Modeling
Given a dataset $D =\lbrace (\textbf {q}^{(d)}, \lbrace a_{1}^{(d)},   , a_{N}^{(d)}\rbrace ) \rbrace _{d}^{\mid D \mid }$ consisting of pairs of questions stems $\textbf {q}$ and answer choices $a_{i}$, the goal is to find the correct answer $a_{i^{*}}$ that correctly answers each $\textbf {q}$  Throughout this paper, we look at 5-way multiple-choice problems (i e , where each $N=5$) 
Probing Methodology and Modeling ::: Task Definition and Modeling ::: Question+Answer Encoder 
To model this, our investigation centers around the use of the transformer-based BIBREF25 BERT encoder and fine-tuning approach of BIBREF7 (see also BIBREF6)  For each question and individual answer pair $q^{(j)}_{a_{i}}$, we assume the following rendering of this input:
which is run through the pre-trained BERT encoder to generate a representation for $ q^{(j)}_{a_{i}}$ using the hidden state representation for CLS (i e , the classifier token) $\textbf {c}_{i}$:
The probability of a given answer $p^{(j)}_{i}$ is then computed as $p^{(j)}_{i} \propto e^{\textbf {v}\cdot \textbf {c}^{(j)}_{i}}$, which uses an additional set of classification parameters $\textbf {v} \in \mathbb {R}^{H}$ that are optimized (along with the full transformer network) by taking the final loss of the probability of each correct answer $p_{i^{*}}$ over all answer choices:
We specifically use BERT-large uncased with whole-word masking, as well as the RoBERTa-large model from BIBREF9, which is a more robustly trained version of the original BERT model  Our system uses the implementations provided in AllenNLP BIBREF39 and Huggingface BIBREF40 
Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks 
When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42)  To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models 
Following the notation from BIBREF0, for any given sequence $s$ of tokens in $\lbrace q^{(j)}, a_{1}^{(j)},   ,a_{N}^{(j)}\rbrace $ in $D$, an encoding of $s$ is given as $h_{s}^{(j)} = \textbf {BiLSTM}(\textsc {embed}(s)) \in \mathbb {R}^{|s| \times 2h}$ (where $h$ is the dimension of the hidden state in each directional network, and embed$(\cdot )$ is an embedding function that assigns token-level embeddings to each token in $s$)  A contextual representation for each $s$ is then built by applying an element-wise max operation over $h_{s}$ as follows:
With these contextual representations, different baseline models can be constructed  For example, a Choice-Only model, which is a variant of the well-known hypothesis-only baseline used in NLI BIBREF46, scores each choice $c_{i}$ in the following way:
for $\textbf {W}^{T} \in \mathbb {R}^{2h}$ independently of the question and assigns a probability to each answer $p_{i}^{(j)} \propto e^{\alpha _{i}^{(j)}}$ 
A slight variant of this model, the Choice-to-choice model, tries to single out a given answer choice relative to other choices by scoring all choice pairs $\alpha _{i,i^{\prime }}^{(j)} = \textsc {Att}(r^{(j)}_{c_{i}},r^{(j)}_{c_{i^{\prime }}}) \in \mathbb {R}$ using a learned attention mechanism Att and finding the choice with the minimal similarity to other options (for full details, see their original paper)  In using these partial-input baselines, which we train directly on each target probe, we can check whether systematic biases related to answer choices were introduced into the data creation process 
A Question-to-choice model, in contrast, uses the contextual representations for each question and individual choice and an attention model Att model to get a score $\alpha ^{(j)}_{q,i} = \textsc {Att}(r^{(j)}_{q},r^{(j)}_{c_{i}}) \in \mathbb {R}$ as above  Here we also experiment with using ESIM BIBREF47 to generate the contextual representations $r$, as well as a simpler VecSimilarity model that measures the average vector similarity between question and answer tokens: $\alpha ^{(j)}_{q,i} = \textsc {Sim}(\textsc {embed}(q^{(j)}),\textsc {embed}(c^{(j)}_{i}))$  In contrast to the models above, these sets of baselines are used to check for artifacts between questions and answers that are not captured in the partial-input baselines (see discussion in BIBREF49) and ensure that the overall MCQA tasks are sufficiently difficult for our transformer models 
Probing Methodology and Modeling ::: Inoculation and Pre-training
Using the various models introduced above, we train these models on benchmark tasks in the science domain and look at model performance on our probes with and without additional training on samples of probe data, building on the idea of inoculation from BIBREF23  Model inoculation is the idea of continuing to train models on new challenge tasks (in our cases, separately for each probe) using only a small amount of examples  Unlike in ordinary fine-tuning, the goal is not to learn an entirely re-purposed model, but to improve on (or vaccinate against) particular phenomena (e g , our synthetic probes) that potentially deviate from a model's original training distribution (but that nonetheless might involve knowledge already contained in the model) 
In the variant proposed in BIBREF22, for each pre-trained (science) model and architecture $M_{a}$ we continue training the model on $k$ new probe examples (with a maximum of $k=$ 3k) under a set of different hyper-parameter configurations $j \in \lbrace 1,    , J\rbrace $ and identify, for each $k$, the model $M_{*}^{a,k}$ with the best aggregate performance $S$ on the original (orig) and new task:
As in BIBREF22, we found all models to be especially sensitive to different learning rates, and performed comprehensive hyper-parameters searches that also manipulate the number of iterations and random seeds used 
Using this methodology, we can see how much exposure to new data it takes for a given model to master a new task, and whether there are phenomena that stress particular models (e g , lead to catastrophic forgetting of the original task)  Given the restrictions on the number of fine-tuning examples, our assumption is that when models are able to maintain good performance on their original task during inoculation, the quickness with which they are able to learn the inoculated task provides evidence of prior competence, which is precisely what we aim to probe  To measure past performance, we define a model's inoculation cost as the difference in the performance of this model on its original task before and after inoculation 
We pre-train on an aggregated training set of the benchmark science exams detailed in Table TABREF21, and created an aggregate development set of around 4k science questions for evaluating overall science performance and inoculation costs  To handle the mismatch between number of answer choices in these sets, we made all sets 5-way by adding empty answers as needed  We also experimented with a slight variant of inoculation, called add-some inoculation, which involves balancing the inoculation training sets with naturalistic science questions  We reserve the MCQL dataset in Table TABREF21 for this purpose, and experiment with balancing each probe example with a science example (x1 matching) and adding twice as many science questions (x2 matching, up to 3k) for each new example 
Probing Methodology and Modeling ::: Evaluating Model Competence
The standard way to evaluate our MCQA models is by looking at the overall accuracy of the correct answer prediction, or what we call instance-level accuracy (as in Table TABREF25)  Given the nature of our data and the existence of semantic clusters as detailed in Section SECREF11 (i e , sets of questions and answers under different distractor choices and inference complexity), we also measure a model's cluster-level (or strict cluster) accuracy, which requires correctly answering all questions in a cluster  Example semantic clusters are shown in Table TABREF30; in the first case, there are 6 ISA$^\uparrow $ questions (including perturbations) about the concept trouser n 01 (e g , involving knowing that trousers are a type of consumer good and garment/clothing), which a model must answer in order to receive full credit 
Our cluster-based analysis is motivated by the idea that if a model truly knows the meaning of a given concept, such as the concept of trousers, then it should be able to answer arbitrary questions about this concept without sensitivity to varied distractors  While our strict cluster metric is simplistic, it takes inspiration from work on visual QA BIBREF53, and allows us to evaluate how consistent and robust models are across our different probes, and to get insight into whether errors are concentrated on a small set of concepts or widespread across clusters 
Results and Findings
In this section, we provide the results of the empirical questions first introduced in Figure FIGREF1, starting with the results of our baseline models 
Results and Findings ::: Are our Probes Sufficiently Challenging?
As shown in Table TABREF25, most of our partial-input baselines (i e , Choice-Only and Choice-to-Choice models) failed to perform well on our dataset probes across a wide range of models, showing that such probes are generally immune from biases relating to how distractors were generated  As already discussed in Section SECREF13, however, initial versions of the DictionaryQA dataset had unforeseen biases partly related to whether distractors were sampled from entries without example sentences, which resulted in high Choice-Only-GloVe scores ranging around 56% accuracy before a filtering step was applied to remove these distractors 
We had similar issues with the hypernymy probe which, even after a filtering step that used our Choice-to-Choice-GloVe model, still leads to high results on the BERT and RoBERTa choice-only models  Given that several attempts were made to entirely de-duplicate the different splits (both in terms of gold answers and distractor types), the source of these biases is not at all obvious, which shows how easy it is for unintended biases in expert knowledge to appear in the resulting datasets and the importance of having rigorous baselines  We also note the large gap in some cases between the BERT and RoBERTa versus GloVe choice-only models, which highlights the need for having partial-input baselines that use the best available models 
Using a more conventional set of Task-Specific QA models (i e , the LSTM-based Question-to-Choice models trained directly on the probes), we can see that results are not particularly strong on any of the datasets, suggesting that our probes are indeed sufficiently challenging and largely immune from overt artifacts  The poor performance of the VecSimilarity (which uses pre-trained Word2Vec embeddings without additional training) provides additional evidence that elementary lexical matching strategies are insufficient for solving any of the probing tasks 
Results and Findings ::: How well do pre-trained MCQA models do?
Science models that use non-transformer based encoders, such as the ESIM model with GloVe and ELMO, perform poorly across all probes, in many cases scoring near random chance, showing limits to how well they generalize from science to other tasks even with pre-trained GloVe and ELMO embeddings  In sharp contrast, the transformer models have mixed results, the most striking result being the RoBERTa models on the definitions and synonymy probes (achieving a test accuracy of 77% and 61%, respectively), which outperform several of the task-specific LSTM models trained directly on the probes  At first glance, this suggests that RoBERTa, which generally far outpaces even BERT across most probes, has high competence of definitions and synonyms even without explicit training on our new tasks 
Given the controlled nature of our probes, we can get a more detailed view of how well the science models are performing across different reasoning and distractor types, as shown in the first column of Figure FIGREF28 for ESIM and RoBERTa  The ESIM science model without training has uniformly poor performance across all categories, whereas the performance of RoBERTa is more varied  Across all datasets and number of hops (i e , the rows in the heat maps), model performance for RoBERTa is consistently highest among examples with random distractors (i e , the first column), and lowest in cases involving distractors that are closest in WordNet space (e g , sister and ISA, or up/down, distractors of distance $k^{\prime }=1$)  This is not surprising, given that, in the first case, random distractors are likely to be the easiest category (and the opposite for distractors close in space), but suggests that RoBERTa might only be getting the easiest cases correct 
Model performance also clearly degrades for hypernymy and hyponymy across all models as the number of hops $k$ increases (see red dashed boxes)  For example, problems that involve hyponym reasoning with sister distractors of distance $k^{\prime }=1$ (i e , the second column) degrades from 47% to 15% when the number of hops $k$ increases from 1 to 4  This general tendency persists even after additional fine-tuning, as we discuss next, and gives evidence that models are limited in their capacity for certain types of multi-hop inferences 
As discussed by BIBREF26, the choice of generation templates can have a significant effect on model performance  The results so far should therefore be regarded as a lower bound on model competence  It is possible that model performance is high for definitions, for example, because the associated templates best align with the science training distribution (which we know little about)  For this reason, the subsequent inoculation step is important—it gives the model an opportunity to learn about our target templates and couple this learned knowledge with its general knowledge acquired during pre-training and science training (which is, again, what we aim to probe) 
Results and Findings ::: Can Models Be Effectively Inoculated?
Model performance after additional fine-tuning, or inoculation, is shown in the last 3 rows of Table TABREF25, along with learning curves shown in Figure FIGREF29 for a selection of probes and models  In the former case, the performance represents the model (and inoculation amount) with the highest aggregate performance over the old task and new probe  Here we again see the transformer-based models outperform non-transformer models, and that better models correlate with lower inoculation costs  For example, when inoculating on synonymy, the cost for ESIM is around 7% reduced accuracy on its original task, as opposed to $< 1$% and around 1% for BERT and RoBERTa, respectively  This shows the high capacity for transformer models to absorb new tasks with minimal costs, as also observed in BIBREF22 for NLI 
As shown in Figure FIGREF29, transformer models tend to learn most tasks fairly quickly while keeping constant scores on their original tasks (i e , the flat dashed lines observed in plots 1-4), which gives evidence of high competence  In both cases, add-some inoculation proves to be a cheap and easy way to 1) improve scores on the probing tasks (i e , the solid black and blue lines in plot 1) and; 2) minimize loss on science (e g , the blue and black dashed lines in plots 2-4)  The opposite is the case for ESIM (plots 5-6); models are generally unable to simultaneously learn individual probes without degrading on their original task, and adding more science data during inoculation confuses models on both tasks 
As shown in Figure FIGREF28, RoBERTa is able to significantly improve performance across most categories even after inoculation with a mere 100 examples (the middle plot), which again provides strong evidence of prior competence  As an example, RoBERTa improves on 2-hop hyponymy inference with random distractors by 18% (from 59% to 77%)  After 3k examples, the model has high performance on virtually all categories (the same score increases from 59% to 87%), however results still tends to degrade as a function of hop and distractor complexity, as discussed above 
Despite the high performance of our transformer models after inoculation, model performance on most probes (with the exception of Definitions) averages around 80% for our best models  This suggests that there is still considerable room for improvement, especially for synonymy and word sense, which is a topic that we discuss more in Section SECREF6 
Results and Findings ::: Are Models Consistent across Clusters?
Table TABREF32 shows cluster-level accuracies for the different WordNetQA probes  As with performance across the different inference/distractor categories, these results are mixed  For some probes, such as definitions, our best models appear to be rather robust; e g , our RoBERTa model has a cluster accuracy of $75\%$, meaning that it can answer all questions perfectly for 75% of the target concepts and that errors are concentrated on a small minority (25%) of concepts  On synonymy and hypernymy, both BERT and RoBERTa appear robust on the majority of concepts, showing that errors are similarly concentrated  In contrast, our best model on hyponymy has an accuracy of 36%, meaning that its errors are spread across many concepts, thus suggesting less robustness 
Table TABREF30 shows a selection of semantic clusters involving ISA reasoning, as well as the model performance over different answers (shown symbolically) and perturbations  For example, in the the second case, the cluster is based around the concept/synset oppose v 06 and involves 4 inferences and a total 24 questions (i e , inferences with perturbations)  Our weakest model, ESIM, answers only 5 out of 24 questions correctly, whereas RoBERTa gets 21/24  In the other cases, RoBERTa gets all clusters correct, whereas BERT and ESIM get none of them correct 
We emphasize that these results only provide a crude look into model consistency and robustness  Recalling again the details in Table TABREF12, probes differ in terms of average size of clusters  Hyponymy, in virtue of having many more questions per cluster, might simply be a much more difficult dataset  In addition, such a strict evaluation does not take into account potential errors inside of clusters, which is an important issue that we discuss in the next section  We leave addressing such issues and coming up with more insightful cluster-based metrics for future work 
Discussion and Conclusion
We presented several new challenge datasets and a novel methodology for automatically building such datasets from knowledge graphs and taxonomies  We used these to probe state-of-the-art open-domain QA models (centering around models based on variants of BERT)  While our general methodology is amendable to any target knowledge resource or QA model/domain, we focus on probing definitions and ISA knowledge using open-source dictionaries and MCQA models trained in the science domain 
We find, consistent with recent probing studies BIBREF26, that transformer-based models have a remarkable ability to answer questions that involve complex forms of relational knowledge, both with and without explicit exposure to our new target tasks  In the latter case, a newer RoBERTa model trained only on benchmark science tasks is able to outperform several task-specific LSTM-based models trained directly on our probing data  When re-trained on small samples (e g , 100 examples) of probing data using variations of the lossless inoculation strategy from BIBREF22, RoBERTa is able to master many aspects of our probes with virtually no performance loss on its original QA task 
These positive results suggest that transformer-based models, especially models additionally fine-tuned on small samples of synthetic data, can be used in place of task-specific models used for querying relational knowledge, as has already been done for targeted tasks such as word sense disambiguation BIBREF54  Since models seem to already contain considerable amounts of relational knowledge, our simple inoculation strategy, which tries to nudge models to bring out this knowledge explicitly, could serve as a cheaper alternative to recent attempts to build architectures that explicitly incorporate structured knowledge BIBREF55; we see many areas where our inoculation strategy could be improved for such purposes, including having more complex loss functions that manage old and new information, as well as using techniques that take into account network plasticity BIBREF56 
The main appeal of using automatically generate datasets is the ability to systematically manipulate and control the complexity of target questions, which allows for more controlled experimentation and new forms of evaluation  Despite the positive results described above, results that look directly at the effect of different types of distractors and the complexity of reasoning show that our best models, even after additional fine-tuning, struggle with certain categories of hard distractors and multi-hop inferences  For some probes, our cluster-based analysis also reveals that errors are widespread across concept clusters, suggesting that models are not always consistent and robust  These results, taken together with our findings about the vulnerability of synthetic datasets to systematic biases, suggest that there is much room for improvement and that the positive results should be taken with a grain of salt  Developing better ways to evaluate semantic clusters and model robustness would be a step in this direction 
We emphasize that using synthetic versus naturalistic QA data comes with important trade-offs  While we are able to generate large amounts of systematically controlled data at virtually no cost or need for manual annotation, it is much harder to validate the quality of such data at such a scale and such varying levels of complexity  Conversely, with benchmark QA datasets, it is much harder to perform the type of careful manipulations and cluster-based analyses we report here  While we assume that the expert knowledge we employ, in virtue of being hand-curated by human experts, is generally correct, we know that such resources are fallible and error-prone  Initial crowd-sourcing experiments that look at validating samples of our data show high agreement across probes and that human scores correlate with the model trends across the probe categories  More details of these studies are left for future work 
Context 2: 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

ground_truth: Unanswerable

Yes

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

Please answer the questions based on the text provided below. How many users do they look at?
Context 2:  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: 22,880 users

20,000

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

Please answer the questions based on the text provided below. Which baselines did they compare against?
Context 3: Introduction
One of the most fundamental topics in natural language processing is how best to derive high-level representations from constituent parts, as natural language meanings are a function of their constituent parts  How best to construct a sentence representation from distributed word embeddings is an example domain of this larger issue  Even though sequential neural models such as recurrent neural networks (RNN) BIBREF0 and their variants including Long Short-Term Memory (LSTM) BIBREF1 and Gated Recurrent Unit (GRU) BIBREF2 have become the de-facto standard for condensing sentence-level information from a sequence of words into a fixed vector, there have been many lines of research towards better sentence representation using other neural architectures, e g  convolutional neural networks (CNN) BIBREF3 or self-attention based models BIBREF4  
From a linguistic point of view, the underlying tree structure—as expressed by its constituency and dependency trees—of a sentence is an integral part of its meaning  Inspired by this fact, some recursive neural network (RvNN) models are designed to reflect the syntactic tree structure, achieving impressive results on several sentence-level tasks such as sentiment analysis BIBREF5 , BIBREF6 , machine translation BIBREF7 , natural language inference BIBREF8 , and discourse relation classification BIBREF9  
However, some recent works have BIBREF10 , BIBREF11 proposed latent tree models, which learn to construct task-specific tree structures without explicit supervision, bringing into question the value of linguistically-motivated recursive neural models  Witnessing the surprising performance of the latent tree models on some sentence-level tasks, there arises a natural question: Are linguistic tree structures the optimal way of composing sentence representations for NLP tasks?
In this paper, we demonstrate that linguistic priors are in fact useful for devising effective neural models for sentence representations, showing that our novel architecture based on constituency trees and their tag information obtains superior performance on several sentence-level tasks, including sentiment analysis and natural language inference 
A chief novelty of our approach is that we introduce a small separate tag-level tree-LSTM to control the composition function of the existing word-level tree-LSTM, which is in charge of extracting helpful syntactic signals for meaningful semantic composition of constituents by considering both the structures and linguistic tags of constituency trees simultaneously  In addition, we demonstrate that applying a typical LSTM to preprocess the leaf nodes of a tree-LSTM greatly improves the performance of the tree models  Moreover, we propose a clustered tag set to replace the existing tags on the assumption that the original syntactic tags are too fined-grained to be useful in neural models 
In short, our contributions in this work are as follows:
Related Work
Recursive neural networks (RvNN) are a kind of neural architecture which model sentences by exploiting syntactic structure  While earlier RvNN models proposed utilizing diverse composition functions, including feed-forward neural networks BIBREF12 , matrix-vector multiplication BIBREF5 , and tensor computation BIBREF6 , tree-LSTMs BIBREF13 remain the standard for several sentence-level tasks 
Even though classic RvNNs have demonstrated superior performance on a variety of tasks, their inflexibility, i e  their inability to handle dynamic compositionality for different syntactic configurations, is a considerable weakness  For instance, it would be desirable if our model could distinguish e g  adjective-noun composition from that of verb-noun or preposition-noun composition, as models failing to make such a distinction ignore real-world syntactic considerations such as `-arity' of function words (i e  types), and the adjunct/argument distinction 
To enable dynamic compositionality in recursive neural networks, many previous works BIBREF14 , BIBREF15 , BIBREF16 , BIBREF9 , BIBREF17 , BIBREF18 , BIBREF19 have proposed various methods 
One main direction of research leverages tag information, which is produced as a by-product of parsing  In detail, BIBREF16 ( BIBREF16 ) suggested TG-RNN, a model employing different composition functions according to POS tags, and TE-RNN/TE-RNTN, models which leverage tag embeddings as additional inputs for the existing tree-structured models  Despite the novelty of utilizing tag information, the explosion of the number of parameters (in case of the TG-RNN) and the limited performance of the original models (in case of the TE-RNN/TE-RNTN) have prevented these models from being widely adopted  Meanwhile, BIBREF9 ( BIBREF9 ) and BIBREF18 ( BIBREF18 ) proposed models based on a tree-LSTM which also uses the tag vectors to control the gate functions of the tree-LSTM  In spite of their impressive results, there is a limitation that the trained tag embeddings are too simple to reflect the rich information which tags provide in different syntactic structures  To alleviate this problem, we introduce structure-aware tag representations in the next section 
Another way of building dynamic compositionality into RvNNs is to take advantage of a meta-network (or hyper-network)  Inspired by recent works on dynamic parameter prediction, DC-TreeLSTMs BIBREF17 dynamically create the parameters for compositional functions in a tree-LSTM  Specifically, the model has two separate tree-LSTM networks whose architectures are similar, but the smaller of the two is utilized to calculate the weights of the bigger one  A possible problem for this model is that it may be easy to be trained such that the role of each tree-LSTM is ambiguous, as they share the same input, i e  word information  Therefore, we design two disentangled tree-LSTMs in our model so that one focuses on extracting useful features from only syntactic information while the other composes semantic units with the aid of the features  Furthermore, our model reduces the complexity of computation by utilizing typical tree-LSTM frameworks instead of computing the weights for each example 
Finally, some recent works BIBREF10 , BIBREF11 have proposed latent tree-structured models that learn how to formulate tree structures from only sequences of tokens, without the aid of syntactic trees or linguistic information  The latent tree models have the advantage of being able to find the optimized task-specific order of composition rather than a sequential or syntactic one  In experiments, we compare our model with not only syntactic tree-based models but also latent tree models, demonstrating that modeling with explicit linguistic knowledge can be an attractive option 
Model
In this section, we introduce a novel RvNN architecture, called SATA Tree-LSTM (Structure-Aware Tag Augmented Tree-LSTM)  This model is similar to typical Tree-LSTMs, but provides dynamic compositionality by augmenting a separate tag-level tree-LSTM which produces structure-aware tag representations for each node in a tree  In other words, our model has two independent tree-structured modules based on the same constituency tree, one of which (word-level tree-LSTM) is responsible for constructing sentence representations given a sequence of words as usual, while the other (tag-level tree-LSTM) provides supplementary syntactic information to the former 
In section 3 1, we first review tree-LSTM architectures  Then in section 3 2, we introduce a tag-level tree-LSTM and structure-aware tag representations  In section 3 3, we discuss an additional technique to boost the performance of tree-structured models, and in section 3 4, we describe the entire architecture of our model in detail 
Tree-LSTM
The LSTM BIBREF1 architecture was first introduced as an extension of the RNN architecture to mitigate the vanishing and exploding gradient problems  In addition, several works have discovered that applying the LSTM cell into tree structures can be an effective means of modeling sentence representations 
To be formal, the composition function of the cell in a tree-LSTM can be formulated as follows:
$$  \begin{bmatrix} \mathbf {i} \\ \mathbf {f}_l \\ \mathbf {f}_r \\ \mathbf {o} \\ \mathbf {g} \end{bmatrix} = \begin{bmatrix} \sigma \\ \sigma \\ \sigma \\ \sigma \\ \tanh \end{bmatrix} \Bigg ( \mathbf {W} \begin{bmatrix} \mathbf {h}_l \\ \mathbf {h}_r \\ \end{bmatrix} + \mathbf {b} \Bigg )$$   (Eq  8)
$$  \mathbf {c} = \mathbf {f}_l \odot \mathbf {c}_l + \mathbf {f}_r \odot \mathbf {c}_r + \mathbf {i} \odot \mathbf {g}\\$$   (Eq  9)
where $\mathbf {h}, \mathbf {c} \in \mathbb {R}^{d}$ indicate the hidden state and cell state of the LSTM cell, and $\mathbf {h}_l, \mathbf {h}_r, \mathbf {c}_l, \mathbf {c}_r \in \mathbb {R}^{d}$ the hidden states and cell states of a left and right child  $\mathbf {g} \in \mathbb {R}^{d}$ is the newly composed input for the cell and $\mathbf {i}, \mathbf {f}_{l}, \mathbf {f}_{r}, \mathbf {o} \in \mathbb {R}^{d}$ represent an input gate, two forget gates (left, right), and an output gate respectively  $\mathbf {W} \in \mathbb {R}^{5d\times 2d}$ and $\mathbf {b} \in \mathbb {R}^{5d}$ are trainable parameters  $\sigma $ corresponds to the sigmoid function, $\tanh $ to the hyperbolic tangent, and $\odot $ to element-wise multiplication 
Note the equations assume that there are only two children for each node, i e  binary or binarized trees, following the standard in the literature  While RvNN models can be constructed on any tree structure, in this work we only consider constituency trees as inputs 
In spite of the obvious upside that recursive models have in being so flexible, they are known for being difficult to fully utilize with batch computations as compared to other neural architectures because of the diversity of structure found across sentences  To alleviate this problem, BIBREF8 ( BIBREF8 ) proposed the SPINN model, which brings a shift-reduce algorithm to the tree-LSTM  As SPINN simplifies the process of constructing a tree into only two operations, i e  shift and reduce, it can support more effective parallel computations while enjoying the advantages of tree structures  For efficiency, our model also starts from our own SPINN re-implementation, whose function is exactly the same as that of the tree-LSTM 
Structure-aware Tag Representation
In most previous works using linguistic tag information BIBREF16 , BIBREF9 , BIBREF18 , tags are usually represented as simple low-dimensional dense vectors, similar to word embeddings  This approach seems reasonable in the case of POS tags that are attached to the corresponding words, but phrase-level constituent tags (e g  NP, VP, ADJP) vary greatly in size and shape, making them less amenable to uniform treatment  For instance, even the same phrase tags within different syntactic contexts can vary greatly in size and internal structure, as the case of NP tags in Figure 1 shows  Here, the NP consisting of DT[the]-NN[stories] has a different internal structure than the NP consisting of NP[the film 's]-NNS[shortcomings] 
One way of deriving structure-aware tag representations from the original tag embeddings is to introduce a separate tag-level tree-LSTM which accepts the typical tag embeddings at each node of a tree and outputs the computed structure-aware tag representations for the nodes  Note that the module concentrates on extracting useful syntactic features by considering only the tags and structures of the trees, excluding word information 
Formally, we denote a tag embedding for the tag attached to each node in a tree as $\textbf {e} \in \mathbb {R}^{d_\text{T}}$   Then, the function of each cell in the tag tree-LSTM is defined in the following way  Leaf nodes are defined by the following:
$$  \begin{bmatrix} \hat{\mathbf {c}} \\ \hat{\mathbf {h}} \\ \end{bmatrix} = \tanh {\left(\mathbf {U}_\text{T} \mathbf {e} + \mathbf {a}_\text{T}\right)}$$   (Eq  13)
while non-leaf nodes are defined by the following:
$$  \begin{bmatrix} \hat{\mathbf {i}} \\ \hat{\mathbf {f}}_l \\ \hat{\mathbf {f}}_r \\ \hat{\mathbf {o}} \\ \hat{\mathbf {g}} \end{bmatrix} = \begin{bmatrix} \sigma \\ \sigma \\ \sigma \\ \sigma \\ \tanh \end{bmatrix} \Bigg ( \mathbf {W_\text{T}} \begin{bmatrix} \hat{\mathbf {h}}_l \\ \hat{\mathbf {h}}_r \\ \mathbf {e} \\ \end{bmatrix} + \mathbf {b}_\text{T} \Bigg )$$   (Eq  14)
$$  \hat{\mathbf {c}} = \hat{\mathbf {f}}_l \odot \hat{\mathbf {c}}_l + \hat{\mathbf {f}}_r \odot \hat{\mathbf {c}}_r + \hat{\mathbf {i}} \odot \hat{\mathbf {g}}\\$$   (Eq  15)
where $\hat{\mathbf {h}}, \hat{\mathbf {c}} \in \mathbb {R}^{d_\text{T}}$ represent the hidden state and cell state of each node in the tag tree-LSTM  We regard the hidden state ( $\hat{\mathbf {h}}$ ) as a structure-aware tag representation for the node  $ \mathbf {U}_\text{T} \in \mathbb {R}^{2d_\text{T} \times d_\text{T}}, \textbf {a}_\text{T} \in \mathbb {R}^{2d_\text{T}}, \mathbf {W}_\text{T} \in \mathbb {R}^{5d_\text{T} \times 3d_\text{T}}$ , and $\mathbf {b}_\text{T} \in \mathbb {R}^{5d_\text{T}}$ are trainable parameters  The rest of the notation follows equations 8 , 9 , and 10   In case of leaf nodes, the states are computed by a simple non-linear transformation  Meanwhile, the composition function in a non-leaf node absorbs the tag embedding ( $\mathbf {e}$ ) as an additional input as well as the hidden states of the two children nodes  The benefit of revising tag representations according to the internal structure is that the derived embedding is a function of the corresponding makeup of the node, rather than a monolithic, categorical tag 
With regard to the tags themselves, we conjecture that the taxonomy of the tags currently in use in many NLP systems is too complex to be utilized effectively in deep neural models, considering the specificity of many tag sets and the limited amount of data with which to train  Thus, we cluster POS (word-level) tags into 12 groups following the universal POS tagset BIBREF20 and phrase-level tags into 11 groups according to criteria analogous to the case of words, resulting in 23 tag categories in total  In this work, we use the revised coarse-grained tags instead of the original ones  For more details, we refer readers to the supplemental materials 
Leaf-LSTM
An inherent shortcoming of RvNNs relative to sequential models is that each intermediate representation in a tree is unaware of its external context until all the information is gathered together at the root node  In other words, each composition process is prone to be locally optimized rather than globally optimized 
To mitigate this problem, we propose using a leaf-LSTM following the convention of some previous works BIBREF21 , BIBREF7 , BIBREF11 , which is a typical LSTM that accepts a sequence of words in order  Instead of leveraging word embeddings directly, we can use each hidden state and cell state of the leaf-LSTM as input tokens for leaf nodes in a tree-LSTM, anticipating the proper contextualization of the input sequence 
Formally, we denote a sequence of words in an input sentence as $w_{1:n}$ ( $n$ : the length of the sentence), and the corresponding word embeddings as $\mathbf {x}_{1:n}$   Then, the operation of the leaf-LSTM at time $t$ can be formulated as,
$$  \begin{bmatrix} \tilde{\mathbf {i}} \\ \tilde{\mathbf {f}} \\ \tilde{\mathbf {o}} \\ \tilde{\mathbf {g}} \end{bmatrix} = \begin{bmatrix} \sigma \\ \sigma \\ \sigma \\ \tanh \end{bmatrix} \Bigg ( \mathbf {W}_\text{L} \begin{bmatrix} \tilde{\mathbf {h}}_{t-1} \\ \mathbf {x}_t \\ \end{bmatrix} + \mathbf {b}_\text{L} \Bigg )$$   (Eq  18)
$$  \tilde{\mathbf {c}}_t = \tilde{\mathbf {f}} \odot \tilde{\mathbf {c}}_{t-1} + \tilde{\mathbf {i}} \odot \tilde{\mathbf {g}}\\$$   (Eq  19)
where $\mathbf {x}_t \in \mathbb {R}^{d_w}$ indicates an input word vector and $\tilde{\mathbf {h}}_t$ , $\tilde{\mathbf {c}}_t \in \mathbb {R}^{d_h}$ represent the hidden and cell state of the LSTM at time $t$ ( $\tilde{\mathbf {h}}_{t-1}$ corresponds to the hidden state at time $t$ -1)  $\mathbf {W}_\text{L}$ and $\mathbf {b}_\text{L} $ are learnable parameters  The remaining notation follows that of the tree-LSTM above 
In experiments, we demonstrate that introducing a leaf-LSTM fares better at processing the input words of a tree-LSTM compared to using a feed-forward neural network  We also explore the possibility of its bidirectional setting in ablation study 
SATA Tree-LSTM
In this section, we define SATA Tree-LSTM (Structure-Aware Tag Augmented Tree-LSTM, see Figure 2 ) which joins a tag-level tree-LSTM (section 3 2), a leaf-LSTM (section 3 3), and the original word tree-LSTM together 
As above we denote a sequence of words in an input sentence as $w_{1:n}$ and the corresponding word embeddings as $\mathbf {x}_{1:n}$   In addition, a tag embedding for the tag attached to each node in a tree is denoted by $\textbf {e} \in \mathbb {R}^{d_\text{T}}$   Then, we derive the final sentence representation for the input sentence with our model in two steps 
First, we compute structure-aware tag representations ( $\hat{\mathbf {h}}$ ) for each node of a tree using the tag tree-LSTM (the right side of Figure 2 ) as follows:
$$  \begin{bmatrix} \hat{\mathbf {c}} \\ \hat{\mathbf {h}} \\ \end{bmatrix} = {\left\lbrace \begin{array}{ll} \text{Tag-Tree-LSTM}(\mathbf {e}) & \text{if a leaf node} \\ \text{Tag-Tree-LSTM}(\hat{\mathbf {h}}_l, \hat{\mathbf {h}}_r, \mathbf {e}) & \text{otherwise} \end{array}\right }$$   (Eq  23)
where Tag-Tree-LSTM indicates the module we described in section 3 2 
Second, we combine semantic units recursively on the word tree-LSTM in a bottom-up fashion  For leaf nodes, we leverage the Leaf-LSTM (the bottom-left of Figure 2 , explained in section 3 3) to compute $\tilde{\mathbf {c}}_{t}$ and $\tilde{\mathbf {h}}_{t}$ in sequential order, with the corresponding input $\mathbf {x}_t$  
$$  \begin{bmatrix} \tilde{\mathbf {c}}_{t} \\ \tilde{\mathbf {h}}_{t} \\ \end{bmatrix} = \text{Leaf-LSTM}(\tilde{\textbf {h}}_{t-1}, \textbf {x}_t)$$   (Eq  24)
Then, the $\tilde{\mathbf {c}}_{t}$ and $\tilde{\mathbf {h}}_{t}$ can be utilized as input tokens to the word tree-LSTM, with the left (right) child of the target node corresponding to the $t$ th word in the input sentence 
$$  \begin{bmatrix} \check{\textbf {c}}_{\lbrace l, r\rbrace } \\ \check{\textbf {h}}_{\lbrace l, r\rbrace } \end{bmatrix} = \begin{bmatrix} \tilde{\textbf {c}}_{t} \\ \tilde{\textbf {h}}_{t} \end{bmatrix}$$   (Eq  25)
In the non-leaf node case, we calculate phrase representations for each node in the word tree-LSTM (the upper-left of Figure 2 ) recursively as follows:
$$  \check{\mathbf {g}} = \tanh {\left( \mathbf {U}_\text{w} \begin{bmatrix} \check{\mathbf {h}}_l \\ \check{\mathbf {h}}_r \\ \end{bmatrix} + \mathbf {a}_\text{w} \right)}$$   (Eq  26)
$$  \begin{bmatrix} \check{\mathbf {i}} \\ \check{\mathbf {f}}_l \\ \check{\mathbf {f}}_r \\ \check{\mathbf {o}} \end{bmatrix} = \begin{bmatrix} \sigma \\ \sigma \\ \sigma \\ \sigma \end{bmatrix} \Bigg ( \mathbf {W_\text{w}} \begin{bmatrix} \check{\mathbf {h}}_l \\ \check{\mathbf {h}}_r \\ \hat{\mathbf {h}} \\ \end{bmatrix} + \mathbf {b}_\text{w} \Bigg )$$   (Eq  27)
where $\check{\mathbf {h}}$ , $\check{\mathbf {c}} \in \mathbb {R}^{d_h}$ represent the hidden and cell state of each node in the word tree-LSTM  $\mathbf {U}_\text{w} \in \mathbb {R}^{d_h \times 2d_h}$ , $\mathbf {W}_\text{w} \in \mathbb {R}^{4d_h \times \left(2d_h+d_\text{T}\right)}$ , $\mathbf {a}_\text{w} \in \mathbb {R}^{d_h}$ , $\mathbf {b}_\text{w} \in \mathbb {R}^{4d_h}$ are learned parameters  The remaining notation follows those of the previous sections  Note that the structure-aware tag representations ( $\hat{\mathbf {h}}$ ) are only utilized to control the gate functions of the word tree-LSTM in the form of additional inputs, and are not involved in the semantic composition ( $\check{\mathbf {g}}$ ) directly 
Finally, the hidden state of the root node ( $\check{\mathbf {h}}_\text{root}$ ) in the word-level tree-LSTM becomes the final sentence representation of the input sentence 
Quantitative Analysis
One of the most basic approaches to evaluate a sentence encoder is to measure the classification performance with the sentence representations made by the encoder  Thus, we conduct experiments on the following five datasets  (Summary statistics for the datasets are reported in the supplemental materials )
MR: A group of movie reviews with binary (positive / negative) classes  BIBREF22
SST-2: Stanford Sentiment Treebank BIBREF6   Similar to MR, but each review is provided in the form of a binary parse tree whose nodes are annotated with numeric sentiment values  For SST-2, we only consider binary (positive / negative) classes 
SST-5: Identical to SST-2, but the reviews are grouped into fine-grained (very negative, negative, neutral, positive, very positive) classes 
SUBJ: Sentences grouped as being either subjective or objective (binary classes)  BIBREF23
TREC: A dataset which groups questions into six different question types (classes)  BIBREF24
As a preprocessing step, we construct parse trees for the sentences in the datasets using the Stanford PCFG parser BIBREF25   Because syntactic tags are by-products of constituency parsing, we do not need further preprocessing 
To classify the sentence given our sentence representation ( $\check{\mathbf {h}}_\text{root}$ ), we use one fully-connected layer with a ReLU activation, followed by a softmax classifier  The final predicted probability distribution of the class $y$ given the sentence $w_{1:n}$ is defined as follows,
$$\mathbf {s} = \text{ReLU}(\mathbf {W}_\text{s} \check{\mathbf {h}}_\text{root}+ \mathbf {b}_\text{s})$$   (Eq  37)
$$p(y|w_{1:n}) = \text{softmax}(\mathbf {W}_\text{c}\mathbf {s} + \mathbf {b}_\text{c})$$   (Eq  38)
where $\textbf {s} \in \mathbb {R}^{d_\text{s}}$ is the computed task-specific sentence representation for the classifier, and $\textbf {W}_\text{s} \in \mathbb {R}^{d_\text{s} \times d_h}$ , $\textbf {W}_\text{c} \in \mathbb {R}^{d_\text{c} \times d_s}$ , $\textbf {b}_\text{s} \in \mathbb {R}^{d_s}$ , $\textbf {b}_\text{c} \in \mathbb {R}^{d_c}$ are trainable parameters  As an objective function, we use the cross entropy of the predicted and true class distributions 
The results of the experiments on the five datasets are shown in table 1   In this table, we report the test accuracy of our model and various other models on each dataset in terms of percentage  To consider the effects of random initialization, we report the best numbers obtained from each several runs with hyper-parameters fixed 
Compared with the previous syntactic tree-based models as well as other neural models, our SATA Tree-LSTM shows superior or competitive performance on all tasks  Specifically, our model achieves new state-of-the-art results within the tree-structured model class on 4 out of 5 sentence classification tasks—SST-2, SST-5, MR, and TREC  The model shows its strength, in particular, when the datasets provide phrase-level supervision to facilitate tree structure learning (i e  SST-2, SST-5)  Moreover, the numbers we report for SST-5 and TREC are competitive to the existing state-of-the-art results including ones from structurally pre-trained models such as ELMo BIBREF26 , proving our model's superiority  Note that the SATA Tree-LSTM also outperforms the recent latent tree-based model, indicating that modeling a neural model with explicit linguistic knowledge can be an attractive option 
On the other hand, a remaining concern is that our SATA Tree-LSTM is not robust to random seeds when the size of a dataset is relatively small, as tag embeddings are randomly initialized rather than relying on pre-trained ones in contrast with the case of words  From this observation, we could find out there needs a direction of research towards pre-trained tag embeddings 
To estimate the performance of our model beyond the tasks requiring only one sentence at a time, we conduct an experiment on the Stanford Natural Language Inference BIBREF34 dataset, each example of which consists of two sentences, the premise and the hypothesis  Our objective given the data is to predict the correct relationship between the two sentences among three options— contradiction, neutral, or entailment 
We use the siamese architecture to encode both the premise ( $p_{1:m}$ ) and hypothesis ( $h_{1:n}$ ) following the standard of sentence-encoding models in the literature  (Specifically, $p_{1:m}$ is encoded as $\check{\mathbf {h}}_\text{root}^p \in \mathbb {R}^{d_h}$ and $h_{1:n}$ is encoded as $\check{\mathbf {h}}_\text{root}^h \in \mathbb {R}^{d_h}$ with the same encoder ) Then, we leverage some heuristics BIBREF35 , followed by one fully-connected layer with a ReLU activation and a softmax classifier  Specifically,
$$\mathbf {z} = \left[ \check{\mathbf {h}}_\text{root}^p; \check{\mathbf {h}}_\text{root}^h; | \check{\mathbf {h}}_\text{root}^p - \check{\mathbf {h}}_\text{root}^h |; \check{\mathbf {h}}_\text{root}^p \odot \check{\mathbf {h}}_\text{root}^h \right]$$   (Eq  41)
$$\mathbf {s} = \text{ReLU}(\mathbf {W}_\text{s} \mathbf {z} + \mathbf {b}_\text{s})$$   (Eq  42)
where $\textbf {z} \in \mathbb {R}^{4d_h}$ , $\textbf {s} \in \mathbb {R}^{d_s}$ are intermediate features for the classifier and $\textbf {W}_\text{s} \in \mathbb {R}^{d_\text{s} \times 4d_h}$ , $\textbf {W}_\text{c} \in \mathbb {R}^{d_\text{c} \times d_s}$ , $\textbf {b}_\text{s} \in \mathbb {R}^{d_s}$ , $\textbf {b}_\text{c} \in \mathbb {R}^{d_c}$ are again trainable parameters 
Our experimental results on the SNLI dataset are shown in table 2   In this table, we report the test accuracy and number of trainable parameters for each model  Our SATA-LSTM again demonstrates its decent performance compared against the neural models built on both syntactic trees and latent trees, as well as the non-tree models  (Latent Syntax Tree-LSTM: BIBREF10 ( BIBREF10 ), Tree-based CNN: BIBREF35 ( BIBREF35 ), Gumbel Tree-LSTM: BIBREF11 ( BIBREF11 ), NSE: BIBREF36 ( BIBREF36 ), Reinforced Self-Attention Network: BIBREF4 ( BIBREF4 ), Residual stacked encoders: BIBREF37 ( BIBREF37 ), BiLSTM with generalized pooling: BIBREF38 ( BIBREF38 ) ) Note that the number of learned parameters in our model is also comparable to other sophisticated models, showing the efficiency of our model 
Even though our model has proven its mettle, the effect of tag information seems relatively weak in the case of SNLI, which contains a large amount of data compared to the others  One possible explanation is that neural models may learn some syntactic rules from large amounts of text when the text size is large enough, reducing the necessity of external linguistic knowledge  We leave the exploration of the effectiveness of tags relative to data size for future work 
Here we go over the settings common across our models during experimentation  For more task-specific details, refer to the supplemental materials 
For our input embeddings, we used 300 dimensional 840B GloVe BIBREF39 as pre-trained word embeddings, and tag representations were randomly sampled from the uniform distribution [-0 005, 0 005]  Tag vectors are revised during training while the fine-tuning of the word embedding depends on the task  Our models were trained using the Adam BIBREF40 or Adadelta BIBREF41 optimizer, depending on task  For regularization, weight decay is added to the loss function except for SNLI following BIBREF42 ( BIBREF42 ) and Dropout BIBREF43 is also applied for the word embeddings and task-specific classifiers  Moreover, batch normalization BIBREF44 is adopted for the classifiers  As a default, all the weights in the model are initialized following BIBREF45 ( BIBREF45 ) and the biases are set to 0  The total norm of the gradients of the parameters is clipped not to be over 5 during training 
Our best models for each dataset were chosen by validation accuracy in cases where a validation set was provided as a part of the dataset  Otherwise, we perform a grid search on probable hyper-parameter settings, or run 10-fold cross-validation in cases where even a test set does not exist 
Ablation Study
In this section, we design an ablation study on the core modules of our model to explore their effectiveness  The dataset used in this experiment is SST-2  To conduct the experiment, we only replace the target module with other candidates while maintaining the other settings  To be specific, we focus on two modules, the leaf-LSTM and structure-aware tag embeddings (tag-level tree-LSTM)  In the first case, the leaf-LSTM is replaced with a fully-connected layer with a $\tanh $ activation or Bi-LSTM  In the second case, we replace the structure-aware tag embeddings with naive tag embeddings or do not employ them at all 
The experimental results are depicted in Figure 3   As the chart shows, our model outperforms all the other options we have considered  In detail, the left part of the chart shows that the leaf-LSTM is the most effective option compared to its competitors  Note that the sequential leaf-LSTM is somewhat superior or competitive than the bidirectional leaf-LSTM when both have a comparable number of parameters  We conjecture this may because a backward LSTM does not add additional useful knowledge when the structure of a sentence is already known  In conclusion, we use the uni-directional LSTM as a leaf module because of its simplicity and remarkable performance 
Meanwhile, the right part of the figure demonstrates that our newly introduced structure-aware embeddings have a real impact on improving the model performance  Interestingly, employing the naive tag embeddings made no difference in terms of the test accuracy, even though the absolute validation accuracy increased (not reported in the figure)  This result supports our assumption that tag information should be considered in the structure 
Qualitative Analysis
In previous sections, we have numerically demonstrated that our model is effective in encouraging useful composition of semantic units  Here, we directly investigate the computed representations for each node of a tree, showing that the remarkable performance of our model is mainly due to the gradual and recursive composition of the intermediate representations on the syntactic structure 
To observe the phrase-level embeddings at a glance, we draw a scatter plot in which a point represents the corresponding intermediate representation  We utilize PCA (Principal Component Analysis) to project the representations into a two-dimensional vector space  As a target parse tree, we reuse the one seen in Figure 1   The result is shown in Figure 4  
From this figure, we confirm that the intermediate representations have a hierarchy in the semantic space, which is very similar to that of the parse tree  In other words, as many tree-structured models pursue, we can see the tendency of constructing the representations from the low-level (the bottom of the figure) to the high-level (the top-left and top-right of the figure), integrating the meaning of the constituents recursively  An interesting thing to note is that the final sentence representation is near that of the phrase `, the stories are quietly moving ' rather than that of `Despite the film's shortcomings', catching the main meaning of the sentence 
Conclusion
We have proposed a novel RvNN architecture to fully utilize linguistic priors  A newly introduced tag-level tree-LSTM demonstrates that it can effectively control the composition function of the corresponding word-level tree-LSTM  In addition, the proper contextualization of the input word vectors results in significant performance improvements on several sentence-level tasks  For future work, we plan to explore a new way of exploiting dependency trees effectively, similar to the case of constituency trees 
Acknowledgments
We thank anonymous reviewers for their constructive and fruitful comments  This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF2016M3C4A7952587) 

ground_truth: Various tree structured neural networks including variants of Tree-LSTM, Tree-based CNN, RNTN, and non-tree models including variants of LSTMs, CNNs, residual, and self-attention based networks

Sentence classification baselines: RNTN (Socher et al. 2013), AdaMC-RNTN (Dong et al. 2014), TE-RNTN (Qian et al. 2015), TBCNN (Mou et al. 2015), Tree-LSTM (Tai, Socher, and Manning 2015), AdaHT-LSTM-CM (Liu, Qiu, and Huang 2017), DC-TreeLSTM (Liu, Qiu, and Huang 2017), TE-LSTM (Huang, Qian, and Zhu 2017), BiConTree (Teng and Zhang 2017), Gumbel Tree-LSTM (Choi, Yoo, and Lee 2018), TreeNet (Cheng et al. 2018), CNN (Kim 2014), AdaSent (Zhao, Lu, and Poupart 2015), LSTM-CNN (Zhou et al. 2016), byte-mLSTM (Radford, Jozefowicz, and Sutskever 2017), BCN + Char + CoVe (McCann et al. 2017), BCN + Char + ELMo (Peters et al. 2018). 
Stanford Natural Language Inference baselines: Latent Syntax Tree-LSTM (Yogatama et al. 2017), Tree-based CNN (Mou et al. 2016), Gumbel Tree-LSTM (Choi, Yoo, and Lee 2018), NSE (Munkhdalai and Yu 2017), Reinforced Self- Attention Network (Shen et al. 2018), Residual stacked encoders: (Nie and Bansal 2017), BiLSTM with generalized pooling (Chen, Ling, and Zhu 2018).