Towards Interpretable Evaluations: A Case Study of Named Entity Recognition

1 INTRODUCTION . The development of deep neural networks has greatly sped the evolution of NLP systems . However , these advances have also come with a plethora of design decisions : should we choose a CNN-based ( Kalchbrenner et al. , 2014 ; Kim , 2014 ) , RNN-based ( Sutskever et al. , 2014 ; Bahdanau et al. , 2014 ) or Transformer-based ( Vaswani et al. , 2017 ; Dai et al. , 2018 ) architecture ? What variety of pre-training method should we use ( Le & Mikolov , 2014 ; Peters et al. , 2018 ; Devlin et al. , 2018 ; Akbik et al. , 2018 ) ? The proliferation of model variants pose a great challenge for current evaluation methodology , which are usually opaque and simply give a single holistic score ( Papineni et al. , 2002 ; Banerjee & Lavie , 2005 ; Popović & Ney , 2011 ) . To alleviate this problem , researchers have made efforts , mainly focusing in two directions . First , some works ( Farrús Cabeceran et al. , 2010 ; Popović & Ney , 2011 ; Lommel et al. , 2014 ) have attempted to shift the granularity of evaluation from holistic to fine-grained by conducting error analysis . Despite its effectiveness , the process of error analysis usually requires manual examination and depends on some pre-existing assumptions , suffering from confirmation bias , and risking ignoring new types of errors ( Neubig et al. , 2019 ) . Additionally , this evaluation method based on error analysis is usually applied to only a single dataset ( Karpathy et al. , 2015 ; Kummerfeld & Klein , 2013 ; Kummerfeld et al. , 2012 ) , lacking discussion of fine-grained analysis in a multi-dataset setting . As a result , many important questions remain unclear : how to characterize the factors that influence the tasks for different datasets ? how do the different choices of datasets influence the models ’ performance ? Another way to improve the evaluation strategy is common in our routine experimental design . That is to evaluate our models on multiple datasets with a holistic metric ( Peters et al. , 2018 ; Devlin et al. , 2018 ) . Currently , researchers are making efforts along this direction by setting up general evaluation benchmarks , such as GLUE ( Wang et al. , 2018 ) and SuperGLUE ( Wang et al. , 2019 ) , which involve a diverse set of datasets . Although it enables us to gain a more comprehensive assessment of the models , the influence of different datasets on models is simply reflected by a holistic metric , which is not interpretable , and consequently , we are not clear about how different datasets influence the choices of model architectures . In this paper , we argue that a complete evaluation method should not only reflect the individual performance of the model on one dataset or multiple datasets but also be able to interpret the model biases , dataset biases , and their correlation ( how the difference in the datasets affects the design of the models ) . We draw on the complementary strengths of the fine-grained evaluation and multidataset evaluation , driving fine-grained analysis to the multi-dataset setting . To this end , we devise a generalized evaluation methodology and choose the NER task as a case study . More specifically , we introduce the notion of attribute , which can be defined flexibly as the evaluation task needs . Here , we utilize the attribute to describe the property of each test entity for the NER task ( i.e. , entity length ) . Then , the test set will be divided into a set of buckets by different attributes of test entities . This makes it possible to evaluate recognition accuracy of different varieties of entities , achieving much more fine-grained analysis than standard corpus-level measures . Additionally , the proposed attribute-aided evaluation methodology encourages us to introduce multiple attributes to find more potential factors which affect the NER models on different datasets . We further propose three analytical approaches as shown in Fig . 1 : attribute-wise , model-wise , and bucketwise analyses that have the following characteristics accordingly : Attribute-wise ( Sec . 3.3.2 ) analysis could instruct us to find which factors matter for the NER tasks and figure out the commonality of factors across different NER datasets ; Model-wise ( Sec . 3.3.1 ) analysis aims to investigate how different attributes influence the performance of models with different architectures and pre-trained knowledge ; Bucket-wise ( Sec . 3.3.3 ) analysis diagnoses the strengths and weaknesses of existing models and helps us understand how different choices of datasets influence the model performance . Our contributions can be precisely summarized as : 1 ) We draw on the complementary strengths of the fine-grained evaluation and multi-dataset evaluation , proposing a generalized evaluation methodology to interpret model biases , dataset biases , and their correlation . 2 ) We choose the NER task as a test case , the observations based on the extensive experiments ( twelve models , ten attributes and six datasets ) suggest directions for improvement and can drive the progress of this area . 3 ) Although some attributes defined in this paper are task-dependent , we claim our methodology is general since : a ) for sequence labeling tasks ( i.e. , Partof-Speech , text chunking , and extractive summarization ) , this evaluation method could be transferred without much modification . b ) for other types of NLP tasks , we could re-define the attributes and our proposed bucketization strategies , as well as three analytic approaches , are task-agnostic . For example , in machine translation , the attribution could be sentence length ( Luong et al. , 2015 ) , word itself ( n-gram ) in the reference file ( Kumar & Tsvetkov , 2018 ) ; In question answering , the attribution could be answer length/type/position ( Seo et al. , 2016 ) , document length ( Joshi et al. , 2017 ) , and query length/type ( Chen et al. , 2016b ) . Once we have determined related attributes , we could make similar analyses based on our proposed measures . 2 PRELIMINARIES . We first summarize and compare past evaluation methodologies and then describe the NER task and its current evaluation strategy . 2.1 PROPERTIES OF EVALUATION METHODOLOGIES . Evaluation is gaining increasing interest in NLP , especially on text generation tasks as exemplified by machine translation ( Fishel et al. , 2012 ; Irvine et al. , 2013 ; Daems et al. , 2014 ) . Generally , different evaluation methods can be characterized by the following three main properties : Interpretability : Evaluation method could give interpretable results to help us understand where the weaknesses and strengths are . For example , “ error analysis ” ( Kummerfeld et al. , 2012 ; Kummerfeld & Klein , 2013 ; Karpathy et al. , 2015 ) is an interpretable evaluation method since we could figure out detailed limitations of the evaluated systems . Confirmation bias : It represents a tendency to make tests consistent with the beforehand hypothesis . For example , Mudrakarta et al . ( 2018 ) assume that deep learning model are sensitive to question words in question answering tasks and verify it by carefully-designed adversarial examples , and Chen et al . ( 2016a ) pre-defined six error to classify error cases . Supplementary exam : It is an additional exam ( require extra test sets ) for more comprehensive observations . i.e . multi-dataset evaluation ( Devlin et al. , 2018 ) on GLUE . Recently , there is a trend going from traditional evaluation to diagnostic test , in which a supplementary test set is required . For example , Jia & Liang ( 2017 ) propose an adversarial test for reading comprehension task and Naik et al . ( 2018 ) present a stress test method to diagnose natural language inference systems . 2.2 NER TASK AND CURRENT EVALUATION STRATEGY . Task Description Named entity recognition ( NER ) is usually formulated as a sequence labeling problem ( Huang et al. , 2015 ; Ma & Hovy , 2016 ) . Formally , let X = { x1 , x2 , . . . , xT } be an input sequence and Y = { y1 , y2 , . . . , yT } be the output tags . The goal of this task is to estimate the conditional probability : P ( Y |X ) = P ( yt|X , y1 , · · · , yt−1 ) Evaluation Strategy for NER Existing NER systems are commonly evaluated by corpus-level metrics ( F1-score ) ( Sang & De Meulder , 2003 ) and a small amount of work will conduct some manual error analysis ( Ichihara et al. , 2015 ; Derczynski et al. , 2015 ) . With the increasing improvement in network architectures and pre-trained knowledge , the NER systems are quickly reaching a performance plateau ( Akbik et al. , 2018 ; Akbik et al. ) . Therefore , fine-grained evaluation is required to identify the specific issues of existing NER systems . On the other hand , with the emergence of more and more NER datasets ( Sang & De Meulder , 2003 ; Collobert et al. , 2011 ; Weischedel et al. , 2013 ) , the time is ripe for us to bridge the gap between the insufficient understanding of the nature of datasets itself and model designs . In this paper , we choose the NER task as a test case , interpreting model biases , dataset biases , as well as their correlation under a general framework . This work also takes a step towards interpretable architecture searching . 3 ATTRIBUTE-AIDED EVALUATION METHODOLOGY . Our proposed evaluation methodology involves three key elements : attribute definition ( Sec . 3.1 ) , bucketization ( Sec . 3.2 ) and the analytical approach ( Sec . 3.3 ) . Specifically , we first introduce the notion of attribute and define it in NER task as some property of the test entity , bywhich the test set will be divided into different sub-sets and the overall performance could be broken down into interpretable categories . Below , we will detail the three key elements . 3.1 DEFINITION OF ENTITY ATTRIBUTES . Entity Attributes refer to the properties that can be used to characterize a given entity . Generally , different types of entity attributes provide different observation angles of system ’ s performances . Next , we will introduce entity attributes we explored in this paper in terms of token level , span level and sentence level . We take them into consideration since they are general features and could be transferred to other tasks . Token-level 1 ) Token itself : it denotes the words of an entity . 2 ) Morphology : morphological features plays an essential role in many NLP tasks . Here , we define five cases for each entity token : including token , is upper case , lower case , digit , beginning with a capital letter , and others ( such as punctuation ) . Span-level 1 ) Entity itself : each entity is regarded as a unique identifier . 2 ) Entity length : the number of tokens in an entity . 3 ) Entity tag : the NER tag of an entity . Sentence-level 1 ) Sentence length ; 2 ) Entity density : it is the number of entity tokens divided by sentence length . 3 ) OOV density : it is the number of oov word divided by sentence length . 3.2 BUCKETIZATION STRATEGIES . Bucketization is an operation that breaks down the holistic performance into different interpretable categories based on the attribute values . This can be achieved by dividing the set of test entities into different subsets of test entities ( for span- and sentence-level attributes ) or test tokens ( for token-level attributes ) . Without loss of generality , we describe the entity-based bucketization strategies while it can be easily applied to token-based . To legibly describe the studied problem , we follow the commonly used notations throughout the paper . We refer to E , P , K as the sets of entities , entity attributes and attributes values , respectively . The bucketization process can be formulated in a general form : Ete1 , · · · , Etem = Bucket ( Ete|Etr , p ) , where Etr , Ete represent the sets of training and test entities , respectively , p denotes a type of the attribute . The basic idea is that the test entity set is divided into m buckets based on the attribute p and corresponding training set . where Etr , Ete represent the sets of training and test entities , respectively , p denotes a type of the attribute . Specifically , each subset of test entities can be obtained as follows : Etei = { e|Atr ( e , p ) ∈ K̂i , ∀e ∈ Ete } , where Atr ( e , p ) is to query out the value of attribute p for entity e. K̂i is a set of attribute values , with which entities should be put into the i-th bucket : K̂i = { k|f ( k ) = i , ∀k ∈ K } . Above equation shows the key part for bucketization : how do we build the relationship between the value of entity attributes and the bucket number ? . That is , we need to determine a criterion f , which guides us to put test entities according to its attribute values into suitable buckets . Here we explore three types of strategies for bucketization . Strategy-I : Range division of attribute values ( R-Bucket ) An intuitive strategy is to bucketize the test entities based on their attribute values directly . Specifically , we could divide the range of attribute values into m discrete parts . For example , the sentence length with range [ 1 , · · · , 6 ] could be divided into [ 1 , 2 ] , [ 3 , 4 ] , [ 5 , 6 ] three buckets . Tab . 2 shows which attributes are matched to this bucketization strategy . Strategy-II : Familiarity of attribute values ( F-Bucket ) The aim of the evaluation is to quantify the generalization errors of the system on the unseen samples . Therefore , by taking into account the degree to which the testing entities ( or their attributes ) have been seen in the training set , we can better figure out the impact of this attribute on model performance . To achieve this , here we introduce a notion of familiarity to quantify the degree to which the attribute of a test entity has been seen in the training set . Fk ( p ) = | { e|Atr ( e , p ) = k , ∀e ∈ Etr } | | { e ∈ Etr } | ( 1 ) Here , Fk ( p ) ∈ [ 0 , 1 ] denotes the degree to which the test entity attribute p with value k have been seen in the training set . Then we can define the following criterion to achieve the bucketization : FK̂i ( p ) ∈ ( i m , i+1 m ] . The basic idea behind the criterion is that test entities could be put into the i-th bucket when the familiarity of their attribute values meets the above condition . Tab . 2 shows which attributes we used for F-Bucket . Strategy-III : Multi-attribute Familiarity ( MF-Bucket ) The benefit of our general methodology for interpretable evaluation is that we can easily define new valuable measures based on old measures already defined . Here , we can adapt our F-Bucket strategies to multi-attribute version and modify the Eq.1 as follows : Fk ( p1 , p2 ) = | { e|Atr ( e , p1 ) = k1 ∧Atr ( e , p2 ) = k2 , ∀e ∈ Etr } | | { Atr ( e , p1 ) = k1 , ∀e ∈ Etr } | ( 2 ) For example , when we instantiate the two entity attributes p1 , p2 as entity itself and tag respectively , the familiarity Fk ( p1 , p2 ) is a measure with intriguing explanation : for each test entity with tag k , this measure quantifies its category ambiguity : the probability that this entity is labeled as k in the training set . To give a more intuitive understanding of the two processes : attribute definition and bucketization , Fig . 2 shows a concrete example , in which a set of attributes are defined for the target entity New York , with corresponding values .

Summary: The manuscript proposes an evaluation methodology to obtain deeper insights regarding the strength and weaknesses of different methods on different datasets. The method considers a set of methods addressing the task of Named Entity Recognition (NER) as case study. In addition, it proposes a set of attribute-based criteria, i.e. bucketization strategies, under which the dataset can be divided and analyzed in order to highlight different properties of the evaluated methods.