Original: Highlights or free-text? A survey on teaching NLP models with human explanationsAnonymous ACL submission
Revised: A survey on improving NLP models with human explanations

Original: Abstract
Revised: Abstract

Original: Providing a model with  human explanationsto learn from can improve data efficiency andmodel performance on in- and out-of-domaindata.
Revised: Training a model with access to human explanations can improve data efficiency and model performance on in- and out-of-domain data.

Original: Adding to these empirical findings, similarity with the process of human learning makeslearning from explanations a promising way toestablish a fruitful human-machine interaction.
Revised: Adding to these empirical findings, similarity with the process of human learning makes learning from explanations a promising way to establish a fruitful human-machine interaction.

Original: Several methods have been proposed for teaching natural language processing (NLP) modelswith human explanations, that rely on differentexplanation types and mechanism for integrating these explanations into the learning process.
Revised: Several methods have been proposed for improving natural language processing (NLP) models with human explanations, that rely on different explanation types and mechanism for integrating these explanations into the learning process.

Original: These methods are rarely compared with eachother, making it hard for practitioners to choosethe best combination of explanation type andintegration mechanism for a specific use-case.
Revised: These methods are rarely compared with each other, making it hard for practitioners to choose the best combination of explanation type and integration mechanism for a specific use-case.

Original: In this paper, we give an overview of differentmethods for learning from human explanations,and discuss different factors that can informthe decision of which method to choose for aspecific use-case.
Revised: In this paper, we give an overview of different methods for learning from human explanations, and discuss different factors that can inform the decision of which method to choose for a specific use-case.

Original:  Introduction
Revised:  Introduction

Original: Enabling machine learning models to learn fromhuman explanations is considered a promising wayfor interaction between human and machine thatcan lead to better models and happier users.
Revised: Enabling machine learning models to learn from human explanations is considered a promising way for interaction between human and machine that can lead to better models and happier users.

Original: If amodel is provided with information about why aspecific prediction should be made for an instance,it can often learn more and faster than if just giventhe correct label assignment (Godbole et al., 2004;
Revised: If a model is provided with information about why a specific prediction should be made for an instance, it can often learn more and faster than if just given the correct label assignment (Godbole et al., 2004;

Original: Zaidan et al., 2007).
Revised: Zaidan et al., 2007).

Original: This reduces the need forannotated data and makes learning from explanations attractive for use-cases with little annotateddata available, for example for adapting models tonew domains (Yao et al., 2021) or for personalizingthem (Kulesza et al., 2015).
Revised: This reduces the need for annotated data and makes learning from explanations attractive for use-cases with little annotated data available, for example for adapting models to new domains (Yao et al., 2021) or for personalizing them (Kulesza et al., 2015).

Original: Human explanationsalso push models to focus on relevant features ofthe data, preventing them from fitting to spuriouscorrelations in the data (Teso and Kersting, 2019).
Revised: Human explanations also push models to focus on relevant features of the data, preventing them from fitting to spurious correlations in the data (Teso and Kersting, 2019).

Original: On top of these beneficial effects on model quality, supervision in the form of explanations is in 
Revised: On top of these beneficial effects on model quality, supervision in the form of explanations is in E - SNLI Premise

Original: [Table]
Revised: [Table]

Original: line with human preferences, as users asked to give feedback to a model want to provide richer feedback than just correct labels (Stumpf et al., 2007;
Revised: line with human preferences, as users asked to give feedback to a model want to provide richer feedback than just correct labels (Stumpf et al., 2007;

Original: Amershi et al., 2014; Ghai et al., 2021).
Revised: Amershi et al., 2014; Ghai et al., 2021).

Original: Several approaches for learning from human explanations have been proposed for different tasks (Table 2), relying on different types of explanations (Table 1), and different methods for integrating them into the learning process.
Revised: Several approaches for learning from human explanations have been proposed for different tasks (Table 2), relying on different types of explanations (Table 1) [Table] and different methods for integrating them into the learning process.

Original: In this paper, we review the literature on learning from highlight and free-text explanations for NLP models, listing technical possibilities and identifying and describing factors that can inform the decision for an optimal learning approach that should optimize both model quality and user satisfaction.
Revised: In this paper, we review the literature on learning from highlight and free-text explanations for NLP models, listing technical possibilities and identifying and describing factors that can inform the decision for an optimal learning approach that should optimize both model quality and user satisfaction.

Revised: Our categorization of methods for integrating explanation information (§ 2.1) is similar to the one provided by Hase and

Revised: Bansal (2021).

Revised: 1 Whereas their categorization fo- cuses on contrasting the approaches according to the role of explanation data in the learning process, we focus on how different types of explanations can be integrated with these approaches.

Original:  Learning from Explanations
Revised:  Learning from Explanations

Original: Highlight and free-text explanations are the most prominent explanation types for teaching NLP models (Wiegreffe and Marasovic, 2021).
Revised: Highlight and free-text explanations are the most prominent explanation types for teaching NLP models (Wiegreffe and Marasovic, 2021).

Original: Highlight explanations  are subsets of input elements that are deemed relevant for a prediction.
Revised: Highlight explanations ( HIGHLIGHT ) are subsets of input elements that are deemed relevant for a prediction.

Original: 1 For textbased NLP tasks, they correspond to sets of words,phrases or sentences.
Revised: For text-based NLP tasks, they correspond to sets of words, phrases or sentences.

Original: Free-text explanations aretexts in natural language that are not constrainedto be grounded in the input elements.
Revised: Free-text explanations ( FREE - TEXT ) are texts in natural language that are not constrained to be grounded in the input elements.

Revised: Some recent works rely on semistructured text explanations ( SEMI - STRUCTURED ) (Wiegreffe and Marasovic, 2021), which combine properties of both highlight and free-text explanations.

Revised: They consist of text in natural language and contain an explicit indication of the input elements that the free-text applies to.

Original: If and howmuch a model can learn from such explanationsdepends on the amount of information containedin the explanation (§ 2.2), and to what extent thisinformation can be integrated into the learning process (§ 2.1).
Revised: If and how much a model can learn from such explanations depends on the amount of information contained in the explanation (§ 2.2), and to what extent this information can be integrated into the learning process (§ 2.1).

Original: User satisfaction is affected by theeffort required to produce explanations and by thedifficulty of the task, that might in turn affect explanation quality (§ 2.3).
Revised: User satisfaction is affected by the effort required to produce explanations and by the difficulty of the task, that might in turn affect explanation quality (§ 2.3).

Original: In the following, we discussthese factors in detail and where possible contrastthem with respect to explanation type.
Revised: In the following, we discuss these factors in detail and where possible contrast them with respect to explanation type.

Original: Approaches for learning from explanations havebeen developed with different objectives in mind,and we introduce the different motivations belowand link them with their respective evaluation in Table 2 ( RESULTS column).Objectives
Revised: Approaches for learning from explanations have been evaluated with different objectives in mind, and we introduce the different motivations below and link them with their respective evaluation in Table 2 ( RESULTS column).

Original: Early works for learning from explanations were motivated by making the learning process more efficient ( EFFICIENCY ).
Revised: Early works for learning from explanations were motivated by making the learning process more efficient ( EFFICIENCY ).

Original: When humanexplanations are provided, models can learn morefrom the same amount of examples (Zaidan et al.,2007), and models can learn more from annotations collected in the same amount of time (Wang*et al., 2020), i.e. human labor can be used moreefficiently.
Revised: When human explanations are provided, models can learn more from the same amount of examples (Zaidan et al., 2007), and models can learn more from annotations collected in the same amount of time (Wang et al., 2020), i.e. human labor can be used more efficiently.

Original: This makes the paradigm useful foruse-cases that allow the collection of additionalannotations.
Revised: This makes the paradigm useful for use-cases that allow the collection of additional annotations.

Original: Information contained in human explanations can make the model generalize betterand lead to better predictive performance on outof-domain data ( OUT - OF - DOMAIN ), which is mostrelevant if the model has to be applied under adistribution shift without access to additional annotations.
Revised: Informa- tion contained in human explanations can make the model generalize better and lead to better predictive performance on out-of-domain data ( OUTOF - DOMAIN ), which is most relevant if the model has to be applied under a distribution shift without access to additional annotations.

Original: Even with large amounts of annotateddata available, models can fit to noise or unwantedbiases in the data (Sun et al., 2019), leading to[Figure] potentially harmful outcomes.Providing human
Revised: Even with large amounts of annotated data available, models can fit to noise or unwanted biases in the data (Sun et al., 2019), leading to potentially harmful outcomes.

Original: [Figure] explanations can prevent a model from fitting to[Figure] such spurious correlations and reduce bias ( BIAS 
Revised: Providing human explanations can prevent a model from fitting to such spurious correlations and reduce bias ( BIAS REDUCTION ).

Original: [Figure]

Original: REDUCTION ). 3 More recently, human explanations [Figure] are used in order to improve model explanations 
Revised: 4 More recently, human explanations have been used in order to improve model explanations ( MODEL EXPLANATION , Strout et al.

Original: [Figure]

Original: ( MODEL EXPLANATION , Strout et al.

Original: or as targets to enable models to generate explanations in the first place (Wiegreffe et al., 2021).
Revised: or as targets to enable models to generate explanations in the first place (Wiegreffe et al., 2021).

Original: 2. Integrating explanation information
Revised: 2. Integrating explanation information

Original: We now give an overview of different methods that are most commonly applied for integrating the information contained in the human explanation into the model ( METHOD column in Table 2).
Revised: We now give an overview of different methodsthat are most commonly applied for integrating the information contained in the human explanation into the model ( METHOD column in Table 2).

Original: Given an input sequence x = ( x 1 , · · · , x L ) of length L , a highlight explanation a is a sequence of attribution scores a = ( a 1 , · · · , a L ) , which is of the same length as x and assigns an importance of a i ∈ R to input element x i .
Revised: Given an input sequence x = ( x 1 , · · · , x L ) of length L , a highlight explanation a is a sequence of attribution scores a = ( a 1 , · · · , a L ) , which is of the same length as x and assigns an importance of a i ∈ R to input element x i .

Original: In practice, a i is often binary.
Revised: In practice, a i is often binary.

Original: A free-text explanation e = ( e 1 , · · · , e M ) is a sequence of words of arbitrary length.
Revised: A free-text explanation e = ( e 1 , · · · , e M ) is a sequence of words of arbitrary length.

Original: Regularizing feature importance
Revised: Regularizing feature importance

Original: This is the dominant approach for learning from highlight explanations.
Revised: This is the dominant approach for learning from highlight explanations.

Original: The model is trained by minimizing an augmented loss function L
Revised: The model is trained by minimizing an augmented loss function L

Original: = L CLS + L EXP composed of the standard cross-entropy classification loss L CLS and an additional explanation loss L EXP .
Revised: = L CLS + L EXP composed of the standard cross-entropy classification loss L CLS and an additional explanation loss L EXP .

Original: Given a sequence m = ( m 1 , · · · , m L ) of attribution scores extracted from the model, the explanation loss is computed by measuring the distance between gold attributions a i and model attribution m i according to L EXP ( a, m ) =
Revised: Given a sequence ˆa = (ˆ a 1 , · · · , ˆ a L ) of attribution scores extracted from the model, the explanation loss is computed by measuring the distance between gold attributions a i and model attribution ˆ a i according to L EXP ( a , ˆa ) =

Original: L (cid:80) i dist ( a i , m i ) .
Revised: L (cid:80) i dist ( a i , ˆ a i ) .

Original: m can be extracted from the model using gradientbased or perturbation-based attribution methods (Atanasova et al., 2020).
Revised: ˆa can be extracted from the model using gradientbased or perturbation-based attribution methods (Atanasova et al., 2020).

Original: Intuitively, the modelis forced to pay attention to input elements that are highlighted in the highlight explanation.
Revised: Intuitively, the model

Original: This method is particularly suited for explanatory debugging, as a user can directly interact with a model by modifying the highlight explanations provided by the model. Semantic parsing to obtain noisy labels This is the dominant approach for learning from freetext explanations.
Revised: [Table]

Original: The information contained in the free-text explanations is made accessible via a semantic parser that maps e to one or more labeling functions λ
Revised: Godbole et al.

Original: i : X → { 0 , 1 } (Ratner et al., 2016). λ i is a logical expression executable on input sequence x and evaluates to 1 if e applies to x , and otherwise. The set of all labeling functions is then used to assign noisy labels to unlabeled sequences for augmenting the training dataset. Existing methods differ in how the labeling functions are applied
Revised: [Table]

Original: [Table]
Revised: [Table]

Original:  et al. 
Revised: Zaidan et al. ■

Original: [Table]
Revised: [Table]

Revised: ■ ▲

Original:  and Eisner (
Revised: Zaidan and Eisner (2008)

Original:  [Table] [Table]
Revised: ■ [Table] ■ ▲

Original:  et al.
Revised: Druck et al.

Revised: (2009) ■

Original: [Table]
Revised: [Table]

Revised: ■ ▲

Original:  et al.
Revised: Small et al.

Original: [Table]
Revised: (2011) ■ ■ ■ ■ ▲

Original: [Table] Settles ( [Table]
Revised: Settles (2011) ■ ■ ■ ▲

Original:  et al.
Revised: Kulesza et al.

Original: [Table]
Revised: ■ ■ ■ ■ ▲

Original:  et al.
Revised: Zhang et al.

Original: [Table] [Table]
Revised: ■ ■ ■ ■ ▲ ▲

Original:  al.
Revised: Bao et al.

Original: [Table] [Table]
Revised: ■ ■ ■ ■ ▲

Original: Zhong et al.
Revised: Zhong et al.

Original: [Table] • Liu and Avci (2019) ■ ■ ■ ■ ▲ ▲
Revised: ■ ■ ■ ■ ▲ • Liu and Avci (2019) ■ ■ ■ ■ ▲ ▲

Original: Strout et al.
Revised: Strout et al.

Original: ■ ■ ■ ■ ▲ ▲
Revised: ■ ■ ■ ■ ▲ ▲

Original: Rieger et al.
Revised: Rieger et al.

Original: (2020) ■ ■ ■ ■ ▲
Revised: (2020) ■ ■ ■ ■ ▲

Original: Stacey et al.
Revised: Stacey et al.

Original: ■ ■ ■ ■ ▲ ▲
Revised: ■ ■ ■ ■ ▲ ▲

Original: Carton et al.
Revised: Carton et al.

Original: ■ ■ ■ ■ ■ ■ ▲ ▲
Revised: ■ ■ ■ ■ ■ ■ ▲ ▲

Original: Mathew et al.
Revised: Mathew et al.

Original: ■ ■ ■ ▲ • Antognini et al.
Revised: ■ ■ ■ ▲ • Antognini et al.

Original: (2021) ■ □ ■ ■ ▲ ▲
Revised: (2021) ■ □ ■ ■ ▲ ▲

Original: Pruthi et al.
Revised: Pruthi et al.

Original: (2020) ■ ■ ■ ■ ■ ■ 
Revised: ■ ■ ■ ■ ■ ■ ▲

Original: Srivastava et al.
Revised: Srivastava et al.

Original: (2017) ■ ■ ■ ■ ■ ▲ ▲
Revised: (2017) ■ ■ ■ ■ ■ ▲ ▲

Original: Hancock et al.
Revised: Hancock et al.

Original: ■ ■ ■ ■ ▲
Revised: ■ ■ ■ ■ ▲

Original: Wang* et al.
Revised: Wang et al.

Original: (2020) ■ ■ ■ ■ ■ ▲
Revised: (2020) ■ ■ ■ ■ ■ ▲

Original: Lee et al.
Revised: Lee et al.

Original: (2020) ■ ■ ■ ■ ■ ■ ■ ▲
Revised: (2020) ■ ■ ■ ■ ■ ■ ■ ▲

Original: Ye et al.
Revised: Ye et al.

Original: (2020) ■ ■ ■ ■ ▲
Revised: (2020) ■ ■ ■ ■ ▲

Original: Murty et al.
Revised: Murty et al.

Original: (2020) ■ ■ ■ ■ ▲
Revised: (2020) ■ ■ ■ ■ ▲

Original: Yao et al.
Revised: Yao et al.

Original: ■ ■ ■ ■ ■ ▲ ▲ ▲
Revised: ■ ■ ■ ■ ■ ▲ ▲ ▲

Original: Camburu et al.
Revised: Camburu et al.

Original: ■ ■ ■ ■ ■ • • ▲
Revised: ■ ■ ■ ■ ■ • • ▲

Original: Rajani et al.
Revised: Rajani et al.

Original: (2019b)

Original: ■ ■ ■ ■ • ▲ ▲
Revised: ■ ■ ■ ■ • ▲ ▲

Original: Kumar and Talukdar (2020) ■ ■ ■ ■ ▼ ▲ ▲
Revised: Kumar and Talukdar (2020) ■ ■ ■ ■ ▼ ▲ ▲

Original: Zhao and Vydiswaran (2021) ■ ■ ■ ■ ■ •  the evaluated quantity compared to a baseline without access to explanations. 
Revised: Zhao and Vydiswaran (2021) ■ ■ ■ ■ ■ • • ) in the evaluated quantity compared to a baseline without access to explanations. is forced to pay attention to input elements that are highlighted in the highlight explanation.

Revised: This method is particularly suited for explanatory debugging, as a user can directly interact with a model by modifying the highlight explanations provided by the model.

Revised: Semantic parsing to obtain noisy labels

Revised: This is the dominant approach for learning from freetext explanations.

Revised: The information contained in the free-text explanations is made accessible via a semantic parser that maps e to one or more labeling functions λ

Original:  to assign noisy labels, e.g. by aggregating scoresover multiple outputs or fuzzily matching input sequences.
Revised: i : X → { 0 , 1 } (Ratner et al., 2016). λ i is a logical expression executable on input sequence x and evaluates to 1 if e applies to x , andotherwise. The set of all labeling functions is then used to assign noisy labels to unlabeled sequences for augmenting the training dataset. Existing methods differ in how the labeling functions are applied to assign noisy labels, e.g. by aggregating scores over multiple outputs or fuzzily matching input sequences.

Original: The approach hinges on the availabilityof a semantic parser, but several works suggest thatusing a relatively simple to adapt rule-based parseris sufficient for obtaining decent results (Hancocket al., 2018). 
Revised: The approach hinges on the availability of a semantic parser, but several works suggest that using a relatively simple to adapt rule-based parser is sufficient for obtaining decent results (Hancock et al., 2018). Table 2 refers to this approach as

Original:  Multi-task learning
Revised: DATA AUGMENTATION . Multi-task learning

Original: In the multi-task learningapproach (Caruana, 1997), two modelsM CLS and
Revised: In the multi-task learning (MTL) approach (Caruana, 1997), two models

Original: M  EXP are trained simultaneously, one for solvingthe target task and one for producing explanations,with most of their parameters being shared betweenthem.
Revised: M CLS and M EXP are trained simultaneously, one for solving the target task and one for producing explanations, with most of their parameters being shared between them.

Original: When learning from highlight explanations, M EXP is a token-level classifier trained to solve asequence labeling task to predict a .
Revised: When learning from high- light explanations, M EXP is a token-level classifier trained to solve a sequence labeling task to predict a .

Original: For learningfrom free-text explanations, M EXP is a languagegeneration model trained to generate e .
Revised: For learning from free-text explanations, M EXP is a language generation model trained to generate e .

Original: Explain andpredict
Revised: Explain and

Revised: predict

Original: This method was introduced explicitly to improve interpretability of the model, rather than learning from human explanations to improve the target task (Lei et al., 2016).
Revised: This method was introduced explicitly to improve interpretability of the model, rather than learning from human explanations to improve the target task (Lei et al., 2016).

Original: The idea is to first have the model produce an explanation based on the input instance ( I → EX ), and then predict the output from the explanation alone ( EX → O ), which is meant to assure that the generated explanation is relevant to the model prediction.
Revised: The idea is to first have the model produce an explanation based on the input instance ( I → EX ), and then predict the output from the explanation alone ( EX → O ), which is meant to assure that the generated explanation is relevant to the model prediction.

Original: The approach can be used for both learning from highlight and free-text explanations.
Revised: The approach can be used for both learning from highlight and free-text explanations.

Original: In contrast to the other methods described previously, explain and predict pipelines require explanations at test time.
Revised: In contrast to the other methods described previously, explain and predict pipelines require explanations at test time.

Original: The human explanations are used to train the I → EX component, which provides the EX → Ocomponent with model explanations at test time.
Revised: The human explanations are used to train the I → EX component, which provides the EX → O component with model explanations at test time.

Original: Comparative studies
Revised: Comparative studies

Original: We found almost no worksthat compare approaches for learning from explanations across integration methods or explanation types.
Revised: We found almost no works that empirically compare approaches for learning from explanations across integration methods or explanation types.

Original: Pruthi et al.
Revised: Pruthi et al.

Original: (2020) compare MTLand REGULARIZATION methods for learning from HIGHLIGHT explanations.
Revised: (2022) compare MTL and REGULARIZATION methods for learning from HIGHLIGHT explanations.

Original: They find that the former method requires more training examples andslightly underperforms regularization.
Revised: They find that the former method requires more training examples and slightly underperforms regularization.

Original: Stacey et al.(2021) evaluate their REGULARIZATION methodfor both HIGHLIGHT and FREE - TEXT explanations.
Revised: Stacey et al. (2022) evaluate their REGULARIZATION method for both HIGHLIGHT and FREE - TEXT explanations.

Original: Results are similar for both explanation types,which might be due to the fact that explanationsare from the E - SNLI dataset, where annotators wereencouraged to include words contained in the highlight explanation into their free-text explanations.
Revised: Results are similar for both explanation types, which might be due to the fact that explanations are from the E - SNLI dataset, where annotators were encouraged to include words contained in the highlight explanation into their freetext explanations.

Original: 2. Information content
Revised: 2. Information content

Original: Few works have focused on quantifying or characterizing the information contained in  a humanexplanation, and how it affects model quality.
Revised: Besides the choice of method for integrating explanation information, another important factor affecting model performance relates to the information contained in the explanation. Ideally, we could define specific criteria that determine if an explanation is useful for solving a task, and use these criteria for selecting or generating the most beneficial explanations, e.g. as part of annotation guide- lines for collecting explanation annotations. In the following, we summarize findings of recent works that provide insights for identifying such criteria.

Original: Haseand Bansal (2021) hypothesize that a model cansuccessfully learn from an explanation if the explanation provides information that is not accessiblefrom the input instance directly.
Revised: Hase and Bansal (2021) hypothesize that a model can be improved based on explanations if they contain additional information that is not accessible from the input instance directly.

Original: Focusing on highlight explanations, Carton et al. (2021) find thatmodel accuracy can be boosted by selecting explanations that lead to accurate model predictionsif presented to the model in isolation. 
Revised: Carton et al. (2021) find that models can profit from those highlight explanations which lead to accurate model predictions if presented to the model in isolation. Carton et al.

Original: They further observe that not all highlighted words affectmodel performance equally (false positives causeless degradation than false negatives). Carton et al.(2020) evaluate human highlight explanations withrespect to their comprehensiveness and sufficiency(Yu et al., 2019), and find thatthey do not necessarily fulfill these criteria. In addition, they observethat it is possible to impove model performancewith ’insufficient’ highlight explanations. 
Revised: (2020) evaluate human highlight explanations with respect to their comprehensiveness and sufficiency, two metrics usually applied to evaluate the quality of model explanations (Yu et al., 2019), and observe that it is possible to improve model performance with ’insufficient’ highlight explanations. In addition, they find that human explanations do not necessarily fulfill these two criteria, indicating that they are not suited for identifying useful human explanations to learn from.

Original: Pruthiet al.
Revised: Pruthi et al.

Original: (2020) suggest to measure explanation quality by calculating the performance difference between a model trained  input instances and trainedwith additional explanation annotations.
Revised: (2022) suggest to quantify explanation quality by calculating the performance difference between a model trained on input instances and trained with additional explanation annotations.

Original: As thelatter three measures depend on a machine learningmodel, they cannot completely disentangle the effects of information content and how easily it canbe accessed by the model.
Revised: As the criteria listed above depend on a machine learning model, they cannot completely disentangle the effects of information content and how easily this content can be accessed by a model.

Original: From a data-centric perspective, Tan (2021) define three categories basedon the information conveyed in the explanation: Proximal mechanisms convey how to infer a labelfrom the input, evidence conveys relevant tokensin the input (and directly maps to highlight explanations), and procedure conveys step-by-step rules and is related to task instructions.
Revised: To alleviate this issue, we could use model-independent criteria to categorize information content.

Original:  Aggarwal et al.
Revised: For example, Aggarwal et al.

Original: (2021) propose to quantify the information contained in a free-text explanation by calculating the number of distinct words (nouns, verbs, adjectives, and adverbs) per explanation. 
Revised: (2021) propose to quantify the information contained in a free-text explanation by calculating the number of distinct words (nouns, verbs, adjectives, and adverbs) per explanation. Explanation type

Revised: The works described above focus on identifying informative instances of explanations of a given explanation type.

Revised: On a broader level, the information that can possibly be contained in an explanation is constrained by its type.

Original: Jansen et al.
Revised: Highlight explanations cannot carry information beyond the importance of input elements, e.g. worldknowledge relevant to solve the target task. Hence, free-text explanations are assumed to be more suitable for tasks requiring complex reasoning, such as natural language inference or commonsense question answering (Wiegreffe and Marasovic, 2021). While this assumption intuitively makes sense, it would be useful to more formally characterize the information conveyed in an explanation of a spe- cific type, in order to match it with the requirements of a given target task. Tan (2021) define three categories based on the information conveyed in the explanation: Proximal mechanisms convey how to infer a label from the input, evidence conveys relevant tokens in the input (and directly maps to highlight explanations), and procedure conveys step-by-step rules and is related to task instructions.

Original:  (2016) describe a procedure for generating gold explanations covering specific knowledge and inference requirements needed to solve a target task (science exam QA), that might be transferred to other tasks for generating informative explanations.
Revised: Jansen et al. (2016) describe a procedure for generating gold explanations covering specific knowledge and inference requirements needed to solve a target task (science exam QA), that might be transferred to other tasks for generating informative explanations.

Original: 2. Human factors
Revised: 2. Human factors

Original: Providing explanations instead of just label annotations requires some overhead from the user, which might negatively affect them.
Revised: Providing explanations instead of just label annotations requires some overhead from the user, which might negatively affect them.

Original: Zaidan et al.
Revised: Zaidan et al.

Original: (2007) found that providing additional highlight explanations took their annotators twice as long as just providing a label for a document classification task.
Revised: (2007) found that providing additional highlight explanations took their annotators twice as long as just providing a label for a document classification task.

Original: They also point out the necessity to account for human impatience and sloppiness leading to lowquality explanations.
Revised: They also point out the necessity to account for human impatience and sloppiness leading to lowquality explanations.

Original: Tan (2021) list several factors that might limit the use of human-generated explanations, including their incompleteness and subjectivity.
Revised: Tan (2021) list several factors that might limit the use of human-generated explanations, including their incompleteness and subjectivity.

Original: Most importantly, they point out that we cannot expect human explanations to be valid even if the human can assign a correct label, as providing an explanation requires deeper knowledge than label assignment.
Revised: Most importantly, they point out that we cannot expect human explanations to be valid even if the human can assign a correct label, as providing an explanation requires deeper knowledge than label assignment.

Original:  Take-Aways
Revised:  Take-Aways

Original: While many approaches for enabling NLP models to learn from highlight or free-text explanations have been proposed, there is a lack of comparative studies across different explanation types and integration methods that could reveal the most promising setup to proceed with.
Revised: While many approaches for enabling NLP models to learn from highlight or free-text explanations have been proposed, there is a lack of comparative studies across different explanation types and integration methods that could reveal the most promising setup to proceed with.

Original: Initial studies on the relation between explanation properties and effect on model quality suggest that the explanation’s information content plays a central role.
Revised: Initial studies on the relation between explanation properties and effect on model quality suggest that the explanation’s information content plays a central role.

Original: We see a promising avenue in developing modelindependent measures for quantifying information content, which could be used to give annotators detailed instructions on how to generate an informative explanation that can benefit the model, or to filter out invalid explanations that could harm model performance. 
Revised: We see a promising avenue in developing modelindependent measures for quantifying information content, which could be used to give annotators detailed instructions on how to generate an informative explanation that can benefit the model, or to filter out invalid explanations that could harm model performance. Acknowledgments We thank the reviewers for their insightful comments and suggestions.

Revised: The research was funded by the XAINES project (BMBF, 01IW20005)