BROS: A Pre-trained Language Model for Understanding Texts in Document
Authors: Teakgyu Hong
DongHyun Kim
Mingi Ji
Wonseok Hwang
Daehyun Nam
Sungrae Park
Venue:  (2021)
Keywords: Pre-trained model
language model
Document understanding
Document intelligence
OCR

Abstract: Understanding document from their visual snapshots is an emerging and challenging problem that requires both advanced computer vision and NLP methods. Although the recent advance in OCR enables the accurate extraction of text segments, it is still challenging to extract key information from documents due to the diversity of layouts. To compensate for the difficulties, this paper introduces a pre-trained language model, BERT Relying On Spatiality (BROS), that represents and understands the semantics of spatially distributed texts. Different from previous pre-training methods on 1D text, BROS is pre-trained on large-scale semi-structured documents with a novel area-masking strategy while efficiently including the spatial layout information of input documents. Also, to generate structured outputs in various document understanding tasks, BROS utilizes a powerful graph-based decoder that can capture the relation between text segments. BROS achieves state-of-the-art results on four benchmark tasks: FUNSD, SROIE*, CORD, and SciTSR. Our experimental settings and implementation codes will be publicly available.

Under review as a conference paper at ICLR 2021
BROS: A PRE-TRAINED LANGUAGE MODEL FOR UNDERSTANDING TEXTS IN DOCUMENT
Anonymous authors Paper under double-blind review
ABSTRACT
Understanding document from their visual snapshots is emerging and chal- lenging problem that requires both advanced computer vision and NL.P methods. Although the recent advance in OCR enables the accurate extraction of text seg- ments, it is still challenging to extract key information from documents due to the diversity of layouts. To compensate for the difficulties, this paper introduces pre-trained language model, BERT Relying On Spatiality (BROS), that represents and understands the semantics of spatially distributed texts. Different from pre- vious pre-training methods on ID text, BROS is pre-trained on large-scale semi- structured documents with novel area-masking strategy while efficiently includ- ing the spatial layout information of input documents. Also, to generate structured outputs various document understanding tasks, BROS utilizes a powerful graph- based decoder that can capture the relation between text segments. BROS achieves state-of- sfate-of-thh-arr results on four benchmark tasks: FUNSID, SROIE*, CORD, and SciTSR. Our experimental settings and implementation codes will be publicly available.
INTRODUCTION
Document intelligence ( which understands industrial documents from their visual appearance, a critical application of in business. One of the important challenges of DI key information extraction task (KIE) (Huang et al. 2019: Jaume et al. 2019 Park et al.) 2019) that extracts struc- tured information from documents such financial reports, invoices, business emails, insurance quotes, and many others. KIE requires multi-disciplinar perspective spanning from computer vision for extracting text from document images to natural language processing for parsing key information from the identified texts.
Optical character recognition (OCR) is a key component to extract texts in document images. As OCR provides set of text blocks consisting of a text and its location, key information in documents can be represented as a single or sequence of the text blocks (Schuster et al. 20133] Qian et al. 2019 Hwang al.) 2019 2020). Although OCR alleviates the burden of processing images, understanding semantic relations between text blocks on diverse layouts remains a challenging problem.
To solve this problem, existing works sentation of text. [waag et al. (2019) fine-tunes BERT by regarding KIE tasks as sequence tagging problems. Denk & Reisswig (2019) uses BERT incorporate textual information into image pixels during their image segmentation tasks. However, since BERT is designed for text sequences, they artificially convert text blocks distributed in two dimensions into single text sequence losing spa- tial layout information. Recently, Xu et al. (2020) proposes LayoutLM pre-trained on large-scale documents by utilizing spatial information of text blocks. They show the effectiveness of the pre- training approach by achieving high performance on several downstream tasks. Despite this success, LayoutLM has three limitations. First, LayoutLM embeds x- and y-axis individually using trainable parameters like the position embedding of BERT, ignoring the gap between positions in a sequence and 2D space. Second, its pre-training method essentially identical to BERT that does not ex- plicitly consider spatial relations between text blocks. Finally, in its downstream tasks, LayoutL only conducts sequential tagging tasks (c.g. BIO tagging) that require serialization of text blocks.
pre-trained language model to utilize its effective repre-

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These limitations indicate that LayoutLM fails not only to fully utilize spatial information but also to address KIE problems in practical scenarios when a serialization of text blocks is difficult.
This paper introduces advanced language model, BROS, pre-trained on large-scale documents, and provides a new guideline for KIE tasks. Specifically, to address the three limitations mentioned above, BROS combines three proposed methods: (1) a 2D positional encoding method that can represent the continuous property of 2D space, (2) a novel area-masking pre-training strategy that performs masked language modeling on 2D, and (3) combination with graph-based decoder for solving KIE tasks. We evaluated BROS on four public KIE datasets: FUNSD (form-like docu- ments), SROIE* (receipts), CORD (receipts). and SciTSR (table structures) and observed that BROS achieved the best results on all tasks. Also, to address KIE problem under a more realistic setting we removed the order information between text blocks from the four benchmark datasets. BROS still shows the best performance on these modified datasets. Further ablation studies provide how each component contributes to the final performances of BROS.
RELATED WORK
2.1
PRE-TRAINED LANGUAGE MODELS
BERT (Devlin et al. 2019) is a pre-trained language model using Transformer (Vaswani et al. 2017) that shows superior performance on various NLP tasks. The main strategy to train BERT is masked language model (MLM) that masks and estimates randomly selected tokens to learn the semantics of language from large-scale corpora. Many variants of BERT have been introduced to learn transferable knowledge by modifying the pre-training strategy. XLNet (Yang et al. 2019)
permutes tokens during the pre-training phase reduce a discrepancy from the fine-tuning phase. XLNet also utilizes relative position encoding to handle long texts. StructBERT (Wang et al., 2020)
shuffles tokens in text spans and adds sentence prediction tasks for recovering the order of words or sentences. SpanBERT (Joshi et al.] (2200) masks span of tokens to extract better representation for span selection tasks such as question answering and co-reference resolution. ELECTRA (Clark et al. 2020) is trained to distinguish real and fake input tokens generated by another network for sample-efficient pre-training.
Inspired by these previous works, BROS utilizes new pre- pre-training strategy that can capture com- plex spatial dependencies between text blocks distributed on two dimensions. Note that LayoutLM is the first pre-trained language model on spatial text blocks but they still employs the original MLM of BERT.
2.2 KEY INFORMATION EXTRACTION FROM DOCUMENTS
Most of the existing approaches utilize a serializer to identify the text order of key information. POT (Hwang et al., 2019) applies BERT serialized text blocks and extracts key contexts via BIO tagging approach. CharGrid (Katii et al., 2018) and BERTGrid (Denk & Reisswig 2019) map text blocks upon a grid space, identify the region of key information, and extract key contexts in the pre-determined order. Liu et al. 20 Yu et al. 2020). and Qian et al. (2019) utilize graph convo- lutional networks to model dependencies between text blocks but their decoder that performs BIO lagging relies on a serialization. LayoutLM (Xu et al. 2020) is pre-trained on large-scale documents with spatial information of text blocks, but it also conducts BIO tagging for their downstream tasks. However, using a serializer and relying on the identified sequence has two limitations. First, the information represented in two dimensional layout can be lost by improper serialization. Second, there may even be no correct serialization order.
natural way to model key contexts from text blocks is a graph-based formulation that identifies all relationships between text blocks. SPADE (Hwang et al. 2020) proposes a graph-based decoder to extract key contexts from identified connectivity between text blocks without any serialization. Specifically, they utilize BERT without its sequential position embeddings and train the model while fine-tuning BERT. However, their performance is limited by the amount of data as all relations have to be learned from the beginning at the BROS is pre- pre-trained on large number of documents and is combined with the SPADE decoder to determine key contexts from text blocks.
stage. To fully utilize the graph-based decoder,
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Token Representation
Transformee
Encoder
Honaaet
JOOMPANT
HAND
Fread Nanager
Transtormer Encoder Block
COMPANY:
Token Embod.
Position Embed.
(token, bb))
Graph-based Classifier
Figure I: Overview of BROS for downstream KIE tasks. Tokens a document are feed into BROS with their bounding box information. After pre-trained with large-scale unlabeled documents, BROS is fine-tuned with a graph-based decoder for document KIE tasks that have small labeled documents.
BERT RELYING ON SPATIALITY (BROS)
The main structure BROS follows BERT, but there are three novel differences: (1) spatial en- coding metric that reflects the continuous property of 2D space, (2) pre-training objective designed for text blocks on 2D space, and (3) guideline for designing downstream models based on graph- based formulation. Figure shows visual description of BROS for document KIE tasks.
3.1 ENCODING SPATIAL INFORMATION INTO BERT
3.1.1 REPRESENTATION OF A TEXT BLOCK LOCATION
The way to represent spatial information of text blocks is important to encode information from layouts. We utilize sinusoidal functions to encode continuous values of x- and y-axis, and merge them through linear transformation to represent point upon 2D space.
For formal description, we use = (æ, y) to denote a point on 2D space and fsinu R + RDD
represent sinusoidal function. D is the dimensions of sinusoid embedding. BROS encodes a 2D point by applying the sinusoidal function to x- and y-axis and concatenating them as = [fsinu (x) + prinu (g)). The symbol indicates concatenation. The bounding box of text block, bb;, consists of four vertices, such pK, pd, p." and p.d that indicate top-left, top-right, bottom-right, and bottom-left points, respectively. The four points are converted into vectors such as p. p. p,y, and p with fsinu, Finally, to represent spatial embedding, bb, BROS combines four identified vectors through a linear transformation,
bb: W' + W W W
(1)
where Wil, W Wbr Wil H2D" are linear transition metrics and H is a hidden size of BERT. The periodic property of the sinusoidal function can encode continuous 2D coordinates more natural than using point-specific embedding used in BERT and LayoutLM. In addition, by learning the linear transition parameters, BROS provides effective representation of bounding box.
3.1.2 ENCODING SPATIAL REPRESENTATION
Position encoding methods affect how models utilize the position information. In BERT, position embedding tied with the token through point-wise summation. However, 2D spatial information is richer than ID sequence due to the their continuous property and the high dimensionality. More- over, text blocks can be placed over various locations on documents without significant changes in its semantic meaning. For example, locations of page numbers differ over multiple document snap- shots even though they are captured from a single document. Therefore. more advanced approach required to maximally include spatial information during encoding beyond the simple summation approach used in BERT. In BROS, the spatial information is directly encoded during the contextual- ization of text blocks. Specifically, BROS calculates attention logit combining both semantic and
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(a) Random token selection and token masking
(h) Random area selection and block masking
Figure 2: Comparison of (a) conventional token masking and (b) area masking, Although there can be multiple tokens in a single text block, the token masking flats all tokens of document, selects a token randomly (red), and masks it directly (black). In contrast, the area masking selects a text block, identifies an area by expanding the block area with an exponential distribution (red), masks tokens in all text blocks of which center is aligned in the identified area (black). In both, 15% of tokens are masked.
spatial features. The former the same as the original attention mechanism in Transformer (Vaswani et al. 2017). but the latter is new component identifying the importance of the target location when the source context and location are given. Our proposed attention logit is formulated as follows,
A. (W*), (W + (W @ W>)lbb;;) (by)lobb;) + (W*96,,) (W**bb;), (2)
where t; and t, are context representations for 7hh and jth tokens and Wa, W W Walkg, W W are linear transition matrices. The @ symbol indicates Hadamard product. The first term indicates attention logit from contextual representations and the third term is from spatial representation. The second term is designed to model the spatial dependency given the source semantic representation, f,, The second and third terms are independently calculated at each layer because spatial dependencies might differ over layers.
3.2 PRE-TRAINING OBJECTIVE: AREA-MASKEI LANGUAGE MODEL
Pre-training diverse layouts from unlabeled documents is key factor for document understanding tasks. To learn effective spatial representation including relationships between text blocks, we pro- pose a novel pre-training objective. Inspired by SpanBERT (Joshi et al., 2020). we expand spans of a ID sequence to consecutive text blocks in 2D space. Specifically, we select a few regions a doc- ument layout, mask all tokens of text blocks in the selected regions, and estimate the masked tokens. The rules for masking tokens in area- masked language model are as the following procedure.
(a) Select a text block randomly and get the lep-left and bottom-right points (p" and p of the block.
(b) Identify the width, height, and center of the block as (w, h) lp" - -p" and c = (p" + )/2
(c) Expand the width and height (w, A) 1 (w, h) where exp(A) and À is a distribution parameter.
(d) Identify rectangular masking area of which top-left and bottom-right are p = pm (w., h), and pmd
px + (1,, ft). respectively.
(c) Mask all tokens of text blocks whose centers are allocated in the area.
Repeat (a)-(e) until 15% of tokens masked.
The rationale behind using exponential distribution is to convert the geometric distribution used in SpanBERT for a discrete domain into distribution for a continuous domain. Thus, we set = IIn(1 p) where p- 0.2 used in SpanBERT. In addition, we truncated exponential distribution with 1 to prevent an infinity multiplier covering all space of the document. It should be noted that the masking area is expanded from randomly selected text block since the area should be related to the text sizes and locations to represent text spans in 2D space. Figure 2 compares token- and area-masking on text blocks.
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Finally, the loss function for the urea-masked language model is formed
LAMIM
Σ #EA(x)
logp(E(x(Att))
where A(x), and Axx) denote tokens in given image, masked tokens of which text block is located in masking area, and the rest tokens, respectively. Similar to BERT (Devlin et al. (201)), the masked tokens are replaced by [MASK] token 80% of the time, random token 10% of the time, or an unchanged token 10% of the time.
3.3 SPATIAL DEPENDENCY PARSERS FOR DOWNSTREAM TASKS
Key information in a document (e.g. store address in receipt) is represented as sub-sequences of text blocks. Although BIO tagging has been used to extract the sub-sequences from text sequence, camnot represent key texts in document without the optimal order of text blocks. Therefore, BIO tagging cannot be applied when the optimal order is not available which often can appear in practical scenario.
To deal with the issue, BROS utilizes a decoder of SPADE (Hwang et al. 2020) that can infer sequence text blocks by employing a graph-based formulation. BROS supports two downstream tasks: (1) entity extraction (EE) task and (2) entity linking (EL) task. The EE identifies sequence of text blocks for key information (e.g. extract address texts in receipt) and the EL determines relations between target texts when target text blocks are known (e.g. identify key and value text pairs).
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(a) Starting token classification
(b) Next token classification
(c) Entity linking (EL) task
Figure 3: Visual descriptions of BROS downstream tasks. For EE tasks, BROS combines two sub- tasks such (a) and (b). For EL task, BROS links the first tokens of the entities.
For EE tasks, BROS divides the problem into two sub-tasks: starting token classification (Figure and subsequent token classification (Figure b). Let L E R denote the 7hh token representation from the last Transformer layer of BROS. The starting token classification conducts token-level tagging determining whether token starting token of target information as follows,
fae (4) softmax W i,)
C+11
where Wsst E (C+1)xH is linear transition matrix and C indicates the number of target classes.
Here, the extra +1 dimension is considered to indicate non-starting tokens.
The subsequent token classification is conducted by utilizing pair-wise token representations as follows,
Fase ( wwf.max T W
Wiit+)))) E R+1
(5)
where Wintess Wi E x are linear transition matrices, Hute is a hidden feature dimension for the next token classification decoder and N the maximum number tokens. Here, nte TR t a model parameter to classify tokens which do not have next token or not related to any class. has a similar role with an end-of-sequence token, [EOS], in NLP. By solving these two sub-tasks, BROS can identify sequence of text blocks by finding first tokens and connecting subsequent tokens.
For EL tasks, BROS conducts binary classification for all possible pairs of tokens (Figure as follows,
fre (T, : i2) sigmoid( (Wooddd((Wees))))) (wiel+ ( (wooh))
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where W Wril R "="" are linear transition matrices and H is hidden feature dimension. Compared to the subsequent token classification, a single token can hold multiple relations with other tokens to represent hierarchical structures of document layouts. For more detail about this graph-based formulation, see Appendix
4
KEY INFORMATION EXTRACTION TASKS
Here, we describe three EE tasks and three EL tasks from four KIE benchmark datasets.
Form Understanding in Noisy Scanned Documents (FUNSD) (Jaume et al. 2019) is a set of documents with various forms. The dataset consists of 149 training and 50 testing examples. FUNSD has both EE and EL tasks. In the EE task, there are three semantic entities: Header, Question, and Answer. In the EL task, the semantic hierarchies are represented as relations between text blocks like header-questien and crestion-answer pairs.
SROIE is variant Task 3 of "Scanned Receipts OCR and Information Extraction" (SROIE F that consists of set of store receipts. In the original SROIE task, semantic contents (Company. Date, Address, and Total price) are generated without explicit connection to the text blocks. To convert SROIE into EE task, we developed SROIE* by matching ground truth contents with text blocks. We also split the original training set into 526 training and 100 testing examples because the ground truths not given in the original test set. SROIE* will be publicly available.
Consolidated Receipt Dataset (CORD) (Park et al. 2019) is a set of store receipts with 800 training. 100 validation, and 100 testing examples. CORD consists of both EE and EL tasks. In the EE task, there are 30 semantic entities including menu name, menu price, and so on. In the EL task, the semantic entities are linked according their layout structure. For example, menu name entities are linked to menu id, menu count, and menu price.
Complicated Table Structure Recognition (SciTSR) (Chi et al. 2019) a EL task that connects cells table to recognize the table structure. There are two types of relations: vertical and horizontal connection between cells. The dataset consists 12,000 training images and 3,000 test images.
Although, these four datasets provide test beds for the EE and EL tasks, they represent the subset of real problems as the optimal order of text blocks given. real service, user can submit documents with a complex layout where the serialization of input texts are non-trivial. FUNSD provides the optimal orders of text blocks related to target classes in both training and testing examples. In SROIE* CORD, and SciTSR, the text blocks are serialized in reading orders.
To reveal the serialization problem in the EE and EL tasks, we randomly permuted text blocks of the datasets to remove their order information. We denote the permuted datasets p-FUNSD, p- SROIE*, p-CORD, and P-SciTSR and compare all models on them. For fair comparisons, we will open the permuted datasets.
EXPERIMENTS
5.1 EXPERIMENT SETTINGS
For mre-training, IIT-CDIP Test Collection 1.0 Lewis et al. 2006). which consists of approximat- ley 1IM document images, is used but 400K RVL-CDIP datase (Harley et al. 2015) is excluded following L.youtLM. In-house OCR engine applied to obtain text blocks from unlabeled docu- ment images.
The main Transformer structure of BROS is the same BERT. By following BERT BASE, the hidden size, the number of self-attention heads, the feed-forward size, and the number of Transformer layers set to 768, 12, 3072, and 12, respectively. The same pre-training setting with LayoutL M is used for a fair comparison.
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Entity Extraction
Entity Linking
Dataset
Model
P
R
F1
P
R
F1
BERT (Xu ct al. 2020
54.69
61.70
60.26
30.89
25.03
27.65
LayoutLM (Xu al. 2020)
75.97
81.55
78.66
FUNSD
LayoutLM
76.12
81.88
78.89
47.80
48.21
48.00
BROS
80.56
81.88
81.21
64.70
70.83
67.63
BERT
92.90
94.47
93.67
SROIE
LayoutI
94.31
95.78
95.04
BROS
94.93
96.03
95.48
BERT
93.08
93.18
93.13
93.03
92.62
92.83
CORD
Lay
95.03
94.58
94.80
93.58
92.72
93.15
BROS
95.58
95.14
95.36
94.37
92.80
93.58
Tabby (Sigarov et al.
2016
92.6
92.0
92.1
DeepDeSRT (Schreiber et al. GraphTSR (Chi et al. 2019)
2017)
90.6
88.7
89.0
95.9
94.8
95.3
SciTSR
BERT
87.61
85.92
86.76
LayoutLMi BROS
98.76
99.44
99.09
99.05
99.47
99.26
Table I: Performance comparisons three EE and three EL tasks with the optimal order informa- tion of text blocks. P, R, and F1 indicate precision, recall, and F1 scores, respectively. For EL tasks connecting entities, SPADE decoder is applied for all pre-trained models such BERT, LayoutL.M, and BROS. The scores are the average of the results with 5 different random seeds.
BROS is trained by using AdamW optimizer (Loshchilov & Hutter 2019) with learning rate of 5e-5 with linear decay. First 10% of the total epochs are used for a warm-up. We initialized weights BROS with those of BERT and trained BROS on IIT-CDIP for epochs with 80 of batch size, following LayoutLM. The pre-training takes 64 hours on 8 NVIDIA Tesla V100s with Distributed Data Parallel (DDP).
During fine-tuning, the learning rate is set 5c-5. The batch size is set 16 for all tasks. The number training epochs or steps is as follows: 100 epochs for FUNSD, 1K steps for SROIE* and CORD, and 7.5 epochs for SciTSR. The hidden feature dimensions, H and Hrl of the SPADE decoder are set to 128 for FUNSD, 64 for SROIE", and 256 for CORD and SciTSR.
Although the authors of LayoutLM published their codes GitH the data and script file used for pre-training are not included. For fair comparison, we made our own implementation, which refer LayoutLM
the pre-training data and script file used for BROS pre-training. We verified LayoutLM by comparing its performances on FUNSD from the reported scores in (Xu al. 2020). See Appendix for more information.
5.2 EXPERIMENTAL RESULTS WITH OPTIMAL ORDER INFORMATION
Table T shows the results on four KIE datasets with given optimal order information text blocks. For EL tasks, applied SPADE decoders to all pre-trained models such BERT, Layout MM, and BROS. In all tasks, we observed that BERT shows lower scores than LayoutI. and BROS presumably due the loss of spatial information. BROS achieves the highest scores showing the effectiveness of our approach. Specifically, in FUNSD, BROS shows the state-of-the-art perfor- mances with large margins of 2.32pp the EE task and 19.63pp in the EL task. Moreover, should be noted that BROS achieves higher F1 score than one of the LayoutLM variants, which utilizes visual features (81.21 > 79.27 (Xu et al. 2020 In SROIE* and CORD, BROS also shows the best performances over all the EE and EL tasks. In SciTSR, LayoutLM and BROS show the im- portance of pre-training by exceeding other baselines with large margins which are trained by using either only spatial information of cells (Tabby and DeT)DeSRT) without pre- mre-training spatial texts

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(GraphTSR). These results prove that BROS captures better representations of text blocks for KIE downstream tasks.
5.3 EXPERIMENTAL RESULTS WITHOUT OPTIMAL ORDER INFORMATION
It is an another challenging problem to arrange text blocks in the order that humans can under- stand (Li al. 2020p. Although most commercial OCR products provide an order OCR text blocks, they are unable to reconcile the structural formatting of the texts precisely (See Appendix [B) Therefore, the experiments in Section 5.2 cannot fully represent real KIE problems because they as- sume the optimal order of text blocks is given. To reveal the challenge, we removed the order information in all datasets by permuting the order of text blocks as mentioned Section and investigated how BERT, LayoutLM, and BROS work without the order information. We utilized a SPADE decoder for all models because BIO tagging on these permuted dataset cannot extract a sequence of text blocks correct order.
Entity Extraction
Entity Linking
Dataset
Model
R
F1
P
R
F1
BERT
21.17
16.99
18.85
14.13
7.26
9.59
P-FUNS
LayoutLM
34.98
32.55
33.72
28.73
23.67
25.95
BROS
74.84
75.50
75.14
63.94
65.53
64.73
BERT
40.65
38.84
39.73
P-SROIE
LayoutLMi
67.42
66.48
66.94
BROS
81.32
81.36
81.34
BERT
59.84
59.58
59.71
31.29
25.14
27.88
p-CORD
LayoutLMt
77.49
77.27
77.38
56.11
53.67
54.86
BROS
93.86
93.68
93.77
84.77
83.15
83.95
BERT
59,42
0.89
1.75
P-SciTSR
LayoutLMi
95.59
99.04
97.28
BROS
98.90
99.32
99.11
Table Performance comparisons on three EE and three EL tasks without the optimal order infor- mation of text blocks. All models utilize the SPADE decoder because BIO tagging cannot be used without the optimal order. The scores the average of the results with different random seeds.
shows the results. Due the lose of correct orders, BERT shows poor performances over all tasks. By utilizing spatial information of text blocks, LayouLLMi shows better performance but it suffers from huge performance degradation compared the score computed with the optimal order. On the other hand, BROS shows comparable results compared the cases with the optimal order and achieves better performances than BERT and LayoutLM..
To systematically investigate how the order information affects the performance of the models, construct variants of FUNSD by re-ordering text blocks with two sorting methods based on the top- left points. The text blocks of Xy-FUNSD y-axis and those FUNSD are sorted according to y-axis with ascending order x-axis.
sorted according to x-axis with ascending order of
Table shows performance on P-FUNSD, Xy-FUNSD, yx-FUNSD, and the original FUNSD. All models utilize a SPADE decoder for a fair comparison. Interestingly, the performance of LayoutLMM M degraded in the order of FUNSD, yX-FUNS, Xy-FUNSD, and P-FUNSD as like the order of the reasonable serialization for text on 2D space. On the other hand, the performance of BROS is relatively consistent. These results show that BROS applicable real KIE problems without relying on an additional serialization method.
5.4 ABLATION STUDIES
Table 4 provides the result of the ablative experiments computed while changing pre- mre-training strat- egy, 2D position embedding and encoding methods, and decoder for downstream tasks. The 2D emhedding method represents how to treat spatial information of text blocks and the 2D encoding
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Model
P-FUNSD
Xy-FUNSD
yx-FUNSD
FUNSD
LayoutLMi
33.72
36.61
60.50
77.30
BROS
75.14
75.24
76.26
81.21
Table Comparison of the EE task performance changes according sorting methods. P-FUNSD and FUNSD indicate permuted and original datasets, repectively. Xy-FUNSD/yx-FUNDS datasets in which text blocks are sorted on x/y-axis with the top-left point of the blocks and then sorted on y/x-axis again if holding the same values. For the same reason in Table 2 all model utilize a SPADE decoder. The scores indicate averages Fl scores for the EE task and they are repeated five times.
represents
MLM
F1
2D position embedding method
F1
Random tokens (LayoutLM's) Random areas
73,85
Look-up tables (LayoutLM's
67.99
74.44
Sinusoid & linear
74.44
2D position encoding method
F1
Decoder for downstream tasks
F1
Tied with token embedding (LayouLLM's) Untied with token embedding
42.46
BIOE tagging (LayoutLM's)
73.16
74.44
SPADE decoder
74.44
Table 4: Ablative studies of the proposed changes from BROS's to LayoutLM'ss the FUNSD EE task. For each ablation, the other components are set as those of BROS. The number of pre-training data is 512K and the scores the average of 5 experimental results.
method indicates how to merge the 2D embeddings into BERT. The results show that all modifica- tions improve performance when comparing the methods of LayoutL.M. Specifically, 2D position embedding and its encoding methods show huge performance gaps by 6.45pp and 31.98pp. respec- tively. These results represent a co-modality of our 2D continuous position embedding approach and its untied encoding method.
LayoutLM and BROS are initialized with weights of BERT to utilize powerful knowledge of BERT that learns from large-scale corpora. However, BERT includes its ID positional embeddings (ID- PE) that might be harmful by making a sequence of text blocks even though there is no order in- formation. To investigate the effectiveness of the 1D-PE, we conduct additional ablative study. BROS without the ID-PE shows the same scores on both FUNSD and p-FUNSD (70.07), but BROS with the ID-PE shows performance degradation when the dataset loses the optimal order information (81.21 on FUNSD + 75.14 14 on P-FUNSD). Nevertheless, BROS with the ID-PE shows better performances on both datasets. This might be because the ID-PE preserves the token order in single text block. Based on this result, we decided to incorporate the 1D-PE in our model.
6 CONCLUSION
We present a novel pre-trained language model, BROS, for understanding semi-structured docu- ments. BROS encodes continuous position of text blocks and learns natural language from text blocks with an area-driven training strategy. To extract key contexts from text blocks without the order information, BROS adapts a graph-based based decoder that identifies text sequences for EE tasks and layout relationships for EL tasks. In extensive experiments on three EE and three EL tasks, BROS consistently shows better performances as well as the robustness on perturbed orders text blocks compared to the existing approaches.

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Under review as a conference paper at ICLR 2021
REFERENCES
Zewen Chi, Heyan Huang, Heng-Da Xu, Houjin Yu, Wanxuan Yin, and Xian-Ling Mao. Compli- cated table structure recognition. arXiv preprint PrXii:99088 04729, 2019.
Kevin Clark, Minh-Thang Luong, Quoe V Le, and Christopher D Manning. ELECTRA: Pre-training text encoders as discriminators rather than generators. In Proceedings of the 8th International Conference on Learning Representations (ICLR), 2020.
Timo I Denk and Christian Reisswig. BERTgrid: Contextualized embedding for 2d document rep- resentation and understanding. In Workshop on Document Intelligence at NeurlPS 2019, 2019.
Jacob Devlin, Ming-Wai Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep
bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Volume I (Long and Short Papers), pp. 4171 4186, 2019.
Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis. Evaluation of deep convolutional nets for document image classification and retrieval. In Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 991-995, 2015.
Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, and CV Jawa- har. ICDAR2019 competition on scanned receipt oct and information extraction. In Proceedings of the 15th International Conference on Document Analysis and Recognition (ICDAR), pp. 1516 1520. IEEE, 2019.
Wonseok Hwang, Seonghyeon Kim, Minjoon Seo, Jinyeong Yim, Seunghyun Park, Sungrae Park, Junyeop Lee, Bado Lee, and Hwalsuk Lee. Post-OCR parsing: building simple and robust parser via bio tagging. In Workshop on Document Intelligence at NeurIPS 2019, 2019.
Wonseok Hwang, Jinyeong Yim, Seunghyun Park, Sohee Yang, and Minjoon Seo. Spatial depen- dency parsing for 2d document understanding, arXiv preprint arXiv:2005.00642, 2020.
Guillaume Jaume, Hazim Kemal Ekenel, and Jean-Philippe Thiran. FUNSD: A dataset for form un- derstanding noisy scanned documents. In 2019 International Conference on Document Analsist and Recognition Workshops (ICDARW), volume 2, pp. 1-6. IEEE, 2019.
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel Weld, Luke Zettlemoyer, and Omer Levy. Span- BERT: Improving pre-training by representing and predicting spans. Transactions of the Associ- ation for Computational Linguisties ( (TACL), 8:64-77, 2020.
Anoop R Katti, Christian Reisswig, Cordula Guder. Sebastian Brarda, Steffen Bickel, Johannes Höhne, and Jean Baptiste Faddoul. Chargrid: Towards understanding 2d documents. Proceed- ings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 4459 4469, 2018
David Lewis, Gady Agam, Shlomo Argamon, Ophir Frieder, D Grossman, and Jefferson Heard. Building a test collection for complex document information processing. In Proceedings of the 29th annual international ACM SIGIR conference Research and development in information retrieval (SIGIR), pp. 665-666, 2006.
Liangcheng Li, Feiyu Gao, Jiajun Bu, Yongpan Wang, Zhi Yu, and Qi Zheng. An end-to-end OCR text re-organization sequence learning for rich-text detail image comprehension. In Proceedings of the 16th European Conference on Computer Vision (ECCV). 2020.
Xiaojing Liu, Feiyu Gao, Qiong Zhang, and Huasha Zhao. Graph convolution for multimodal in- formation extraction from visually rich documents. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HI.T), Volume 2 (Industry Papers). pp. 32-39, 2019.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In Proceedings of the 7th International Conference Learning Representations (ICIR), 2019.
10

---

Under review as a conference paper at ICLR 2021
Seunghyun Park, Seung Shin, Bado Lee, Junyeop Lee, Jacheung Surh, Minjoon Seo, and Hwalsuk Lee. CORD: A consolidated receipt dataset for post-ocr parsing. In Workshop on Document Intelligence at NeurlPS 2019, 2019.
Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo, and Regina Barzilay. GraphIE: graph-based framework for information extraction. In Proceedings of the 2019 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Volume 1 (Long and Short Papers), pp. 751-761, 2019.
Sebastian Schreiber, Stefan Agne, Ivo Wolf, Andreas Dengel, and Sheraz Ahmed. DRT: Deep learning for detection and structure recognition tables in document images. Proceed- ings of the 14th International Conference Document Analysis and Recognition (ICDAR), pp- 1162-1167. IEEE, 2017.
Daniel Schuster, Klemens Muthmann, Daniel Esser, Alexander Schill, Michael Berger, Christoph Weidling, Kamil Aliyev, and Andreas Hofmeier. Intellix-end-user trained information extrac- tion for document archiving. In Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR), Pp. 101-105. IEEE, 2013.
Alexey Shigarov, Andrey Mikhailov, and Andrey Altaev. Configurable table structure recognition in untagged PDF documents. In Proceedings of the 2016 ACM Symposium Document Engi- neering (DocEng). pp. 119-122, 2016.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances Neural Infor- mation Processing Systems 30 (NeurIPS), pp. 5998-6008, 2017.
Wei Wang, Bin Bi, Ming Yan, Chen Wu, Jiangnan Xia, Zuyi Bao, Liwei Peng, and Luo Si. Struct- BERT: Incorporating language structures into pre-training for deep language understanding. In Proceedings of the 8th International Conference Learning Representations (ICLR), 2020.
Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. LayoutLM: Pre- training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference Knowledge Discovery & Data Mining (KDD), pp. 1192- 1200, 2020.
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. XLNet: Generalized autoregressive pretraining for language understanding. In Advances in Neu- ral Information Processing Systems 32 (NeurIPS), pp. 5753-5763, 2019.
Wenwen Yu, Ning Lu, Xianbiao Qi. Ping Gong, and Rong Xiao. PICK: Processing key information extraction from documents using improved graph convingaconvllutional networks. Proceed- ings of the 25th International Conference on Pattern Recognition (ICPR), 2020.

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Under review as a conference paper at ICLR 2021
A REPRODUCING THE LAYOUTLM
As mentioned the paper, to compare BROS from LayoutLM diverse experimental settings, we implement LayoutLM in our experimental pipeline. Table implementations from the reported scores in Xu et al. (2020). As can be seen, multiple experiments are conducted according to the number of pre-training data. Our implementation, referred to LayoutL shows comparable performances over all settings.
# Pre-training Data
Epochs
Model
LayoutLM (Xu et al.
2020
0.5779
0.6955
0.6313
500K
LayoutLM
0.5823
0.6935
0.6330
LayoutLM (Xu et al.
2020
0.6156
0.7005
0.6552
IM
LayoutLM
0.6142
0.7151
0.6608
LayoutLM (Xu et al.
2020
0.6599
0.7355
0.6957
2M
LayoutLM
0.6562
0.7456
0.6980
LayoutLM (Xu et al.
2020
0.7464
0.7815
0.7636
LayoutLM
0.7384
0,8022
0.7689
11M
LayoutLM (Xu et al.
2020
0.7597
0.8155
0.7866
LayoutLM
0.7612
0.8188
0.7889
Table 5: Sanity checking of Lay by comparing its performances on FUNSD from the re- ported scores in Xu et al. (2020).
B VISUALIZATION OF SERIALIZED OCR BLOCKS
(A) Appropriate Reading Order
Nooe of HOOOH Mit BOOIAAA DIaar
Laboratocy
DO CTNE
Housture
Jnalytig
SEOUERE
Venizoo enslysls
Lyel
Requirements Laboratory:
Other: 1100 pts. Laburatory Analysis: Moisture anulysis Menthol analysis
Tar & nicotine analysis
TEA Pager
106
(B) Commercial OCR API
KLnd
Jnalysis:
Sipk
Mizzir
BLOO PiC
Menthol snal snalysig
ans Lvsld
Requirements. Laborntory Analysis:
Laboratory. Moisture analysis Other 1100 pks.
Capte de poited vitt
Menthol analysis analysis
amjie with opticaat wit coss
Cigle io ineooaa, pland of tie talhes
Figure 4: OCR block sequence comparisons between appropriate reading order and commercial OCR (Google Cloud Vision API) on FUNSD training sample. Red text denotes question, green texts denotes answer, circled number denotes appropriate reading order, order of OCR output each.
With the developments in the field of machine learning, the performance of commercial OCR has improved over the years. However, still hard to entrust the ordering commercial OCR block outputs Li al. (2020). Figure 4 shows the gap between the comprehensive reading order and outputs of commercial OCR. Specifically, the figure contrasts how the words in the OCR results should be serialized (Figure 4h) but most commercially available OCR technologies unable to
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reconcile the structural formatting of the text leading them ordering the words horizontally Figuue 4p). This cursory example illustrates that as advanced as commercial OCR solutions have become. there are still ways to improve and proposed method is way in which this can be done.
C ABLATION STUDIES
Here, we provide more ablation studies on the components proposed in the paper. In the following tables, the number of pre-training data is 512K and the scores (FI) are the average of 5 experimental results. And for all the EL tasks, since BIO tagging cannot address the problem, SPADE decoder is applied to models.
C.I FURTHER ABLATION STUDIES ON ALL DOWNSTREAM TASKS
Table and Table 7 the extension of Table and show the Fl scores for all downstream EE and EL tasks measured by changing each components one by one in BROS. From these tables, we can see that the settings of BROS show the best performance most cases.
Model
FUNSD EE
SROIE EE
CORD EE
BROS
74.44
93.99
95.15
(area- gurea-masking + token token- masking)
73.85
93.07
94.89
(sinusoid linear >> look-up table)
67.99
92.22
93.53
(untied + tied)
42.46
26.39
75.21
(SPADE decoder + BIO tagging)
73.16
93.70
94.88
Table 6: Ablation studies on all downstream EE tasks. For each ablation, the other components are set as those of BROS except for the highlighted component.
Model
FUNSD EL
CORD EL
SciTSR EL
BROS (with SPADE decoder) (area-masking - token- masking)
42.48
90.59
99.17
41.92
89.80
99.15
(sinusoid & linear + look-up table)
32.11
86.55
98.65
(untied tied)
27.83
76.35
98.76
Table 7: Ablation studies all downstream EL. tasks. For each ablation, the other components are set as those of BROS except for the highlighted component.
C.2 GRADUALLY ADDING PROPOSED COMPONENTS TO THE ORIGINAL LAYOUTLM
To evaluate performance improvements from LayoutLM, we provide the experimental results when gradually adding each new component. Table g and Table provide performance changes F1 score for EE and EL tasks, respectively. In most cases, our proposed methods show performance improvements over all tasks.
Model
FUNSD EE
SROIE EE
CORD EE
LayoutLM
64.54
93.47
92.81
+ area-masking strategy
65,98
92.97
93.56
+ untied token encoding
66.78
93.70
93.46
+ positional embedding (sinusoid & linear)
73.16
93.70
94.88
+ SPADE decoder (-> BROS)
74.44
93.99
95.15
Table 8: Performance improvements on EE tasks through gradual changes from Layout's method to our method. At the last line, all components are changed from LayoutLM and the model becomes BROS.
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Model
FUNSD EL
CORD EL
SeiTSR EL
LayoutLM (with SPADE decoder)
29.18
86.29
98.35
area-masking strategy
31.74
86.47
98.67
untied token encoding
32.11
86.55
98.65
+ sinusoid & linear (+> BROS)
42.48
90.59
99.17
Table 9: Performance improvements EL tasks through gradual changes from Layout's method to our method. At the last line, all components are changed from LayoutL.M and the model becomes BROS.
C.3 PROPOSED COMPONENTS ON THE ORIGINAL LAYOUTLM
For hoples-to-applee comparison, we provides performance changes when adding each proposed component LayoutLM. The results shown in Table T0 and Table [1] When changing the original module to ours, the performances are solely increased except for the case of the positional embedding (sinusoid & linear). Interestingly, when combining positional embedding and en- coding (untied), the performance is dramatically increased. This result shows the benefits of using our proposed embedding and encoding methods together.
Model
FUNSD EE
SROIE EE
CORD EE
LayoutLM
64.54
93.47
92.81
(token-masking > area-masking)
65.98
92.97
93.56
(tied -> untied)
66.03
94.23
93.38
(look-up table - sinusoid & linear)
49.42
79.16
87.76
(tied + look-up table >> untied sinusoid & linear)
72.48
93.61
94.89
(BIO tagging SPADE decoder)
65.91
92.18
92.93
Table 10: Performance comparison EE tasks when changing from Layout's method method. At each ablation, the other components are set as those of LayoutL.M.
Model
FUNSD EL
CORD EL
SciTSR EL
LayouLLM (with SPADE decoder)
29.18
86.29
98.35
(token-masking + area-masking)
31.74
86.47
98.67
(tied - untied)
32.79
86.60
98.62
(look-up table - sinusoid & linear)
32.31
80.06
98.91
(tied look-up table untied sinusoid & linear)
41.92
89.80
99.15
Table 11: Performance comparison on EL tasks when changing from Layout's method to our method. At each ablation, the other components are set as those of LayoutL.M.
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D RESOURCE ANALYSIS
Table 12 shows the resource and speed analysis of LayoutLMM and BROS. The scores of Lay-
outLM referred from (Xu et 2020) and all pre-training models are trained with epoch of 11M data. As can be seen, BROS shows better performance than LayOutLMAARGE even though requiring fewer parameters and less inference time.
Model
Backbone
# params
inference time (ms)
FUNSD EE
LayoutLMgAsE BROS
BERT BASE
113M
100.46
76.36
BERT BASE
139M
135.36
79.63
LayoutLMM
BERT LARGE
343M
292.08
77.89
Table 12: Resource analysis of LayoutLM and BROS. To measure the inference time, we used NVIDIA Tesla V100 32GB GPU with batch size of 8.
E GRAPH-BASED FORMALIZATION FOR EE AND EL TASKS
Document KIE is a task that extracts structural information from documents. In this paper. we defined EE task that identifies text sequences for target classes and EL task that links the head of the text sequences to determine structural information documents. These EE and EL tasks can be interpreted tasks that identify directional graph structure between OCR text blocks. In this formalization, all tokens are treated as nodes in graph and the links between the nodes indicate the structural relationships between tokens in document. Figure 5 shows examples of FUNSD, SROIE, CORE, and SciTSR with the graph-based formalization.
F SAMPLE INFERENCE RESULTS OF THE FUNSD EE AND EL TASKS
Figure/Blshows the inference results of LayoutL LM and BROS and the ground truth of a same FUNSD image. Even though the document has a complex layout, BROS identified key contexts and relations reasonably. However, we observed that LayoutLM tends to link unrelated contexts that are spatially far in the layout.
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EN EXPRTEE HARILANL AL AHD OPN BHD
(R- SIN ST22400 SENHI GET DO 0013PIS0211S
STALLFTED THE INOCEE Juii Canaler
ESPORDALOR
CRL MLDS DASON AUCIZ
TINE:
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20.003
WATTIT
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SUE TIR (051)
28, CD
ital Itee:
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0.00
TOTAL CHANGE
otal.
22.001
esh
BEET
ndereds
22.000
COTE
5R
3.38
a FUNSD EE task
TAY COTALE
(b) CORD EE task
SO0D SOLD NOT
8.B. PROTTOS OKIO
(c) SROIE EE task
Nagued
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Tatal Iteer
(f) SciTSR EL task
etal.
22.001
ehh
ndered:
22.000
(d) FUNSD EL task
(e) CORD EL task
Figure 5: Examples of EE and EL task definition through graph-based formalization in all down- stream tasks. For all EE tasks, the colored boxes indicate the first boxes of target classes and the green arrows represent next word connections. For example, the first menu in (b) has the first word is "1001-Choco" and its next word is "Bun". For EL tasks, the structural relationship between com- ponents represented with red arrows. For example, the first menu ("1001-Choco Bun"") in (b) has multiple sub information such as unit price ("22.000"), count ("x1"), and total price ("22.000"). SciTSR has two types of relations such as "horizontal" (red) and "vertical" (blue). As shown in the examples, EE and EL tasks can be formed with these graph- based formalization.
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1H AONINCE
(a) EE task LayoutLM
(b) EE task BROS
(c) EE task GT
1200
Stec IncIsDoive
(d) EL task LayoutLM
(e) EL task BROS
( EL task GT
Figure 6: Inference EE and EL results on sample FUNSD image. The images upper row show the results of LayoutLM,, BROS, and ground truth for EE task and the images of lower row show those for task. EE task, the red, green, and blue boxes indicate Question, Answer, ans Header classes, respectively. In EL task, the blue boxes indicate key blocks and the red lines show the relations.
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Reviews:
[{"review_id": "fm02Jtlc8rU", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "### Overall\n\nAuthors used BERT alongside to a 2D-position embedding based on a sinusoidal function and a graph-based decoder to improve performance on document information extraction tasks. They do pre-train their model (BROS) on a large dataset with 11M documents, and then used such models to perform downstream tasks in four smaller datasets. Their models achieve better quantitative results when compared to the provided baselines.\n\n### Positive aspects \n\n* Positional encoder based on sinusoidal function seems to be effective.\n* Authors perform experimentally sound experiments, following closely LayoutLM.\n* Pre-trained models could be useful.\n* Authors reproduced results from their strongest baseline.\n* Better results in all downstream tasks.\n\n### Cons and aspects to improve\n\nMy main concern is that the overall contribution is seems to be limited.In fact, the original paper of the Transformer approach, already proposed such kind of embedding. It is good to know that it works for 2D-coordinates for the task at hand, though it seems to be more a marginal improvement on existing work rather than a standalone contribution.\n\nIt is hard to tell what are the standalone contributions of the paper, and what is coming from other works.\n\nAuthors could have provided more in-depth details (visualizations, analysis, examples) to show main differences between the proposed approach and baselines (specially LayoutLM). Also they could visually demonstrate the advantages of their approach.\n\nAuthors could have plugged their embedding strategy in LayoutLM to understand the impact of that particular component.\n\nI would like to have seen qualitative examples of model predictions, and more examples from the dataset.\n\nA figure containing the whole process could be helpful to better understand the processing required to train / test such models. Figure from LayoutLM is a good example of that, it comprises the entire process and makes it easier to understand the whole architecture.\n\n* In the abstract, authors say \"BROS utilizes a powerful graph-based decoder that can capture the relation between text segment\"* Though in the text such a component (that is from other work) is only mentioned twice without further detail.\n\nIt is unclear to me:\n\n* How regions of interest are detected in this work? (I assumed authors used the same strategy as LayoutLM).\n* OCR seems to be an extra-step in the preprocessing stage. What to do if the user does not have the same OCR. What is the impact of a good OCR for training and testing (prediction of new, unseen documents)?\n* \"In-house OCR engine was applied\" can authors provide more details on that?\n\nThis line of work could be much stronger if the models comprised the whole process (detection, text extraction, recognition) in an end-to-end manner.\n\n### Notes on text and style\n\nThere are parts of the manuscript that felt somewhat informal and confusing to me. I will provide some details as follows.\n\n* Set a default format for numbers in tables. Table 1 has two distinct decimal number formats.\n* Personally, I think it is better to write $5 \\times 10^{-5}$ rather than 5e-5.\n* In the results section there is a typo: *\"performances with a large margins of 2.32pp in\"*. Also, text could be more formal. I would avoid using the use of the *pp* abbreviation.\n* \"By achieving the best, these results prove that BROS\" this sentence can be improved.\n* \"Moreover, it should be noted that BROS achieves higher f1 score than 79.27 of LayoutLM using visual features\". I think authors wanted to say that even though BROS does not rely on visual features, it does outperform LayoutLM which, in turn, uses visual features. \n", "extra_scores": {}, "extra_text": {}}, {"review_id": "cLvMtFUhH1l", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper proposes the pre-trained language model BROS which aims to leverage both text and spatial information to improve information extraction on documents. Using the graph-based decoder from SPADE, BROS achieves the SOTA performance on some entity extraction and entity linking downstream tasks. However, the area-masking strategy does not show significant improvement over the LayoutLM and the graph decoder is proposed in SPADE which is not new. In addition, as most commercial OCR tools have already got very good reading order information, the benefit from the graph decoder might be marginal.\n\nPros\n-\tThe paper introduces the area-masking pre-training strategy that can be seen as a natural generalization of masking language model in the 2D plane.\n-\tThe authors integrate spatial information into the attention mechanism as a pair-wise bias term, which is reasonable.\n-\tBROS utilizes the graph-based decoder from SPADE and improves performance on downstream tasks.\n\nCons\n-\tThe area-masking strategy is to mask small area centered at some tokens, which is actually similar to masking the center token only. Also, given that the FUNSD dataset is small, the area-masking strategy does not show significant improvement over vanilla MLM.\n-\tThis paper shows that sinusoid & linear functions can encode 2D position efficiently. However, it is not reasonable to compare sinusoid & linear and learnable embeddings on small data, since learnable embeddings could leverage large amount of data and get more gains.\n-\tThe graph-based decoder part is identical to which in SPADE so it is not suitable to appear as the contributions of this paper.\n\nIn summary, this paper largely overlaps with the previous research work. I do not think it is qualified for the ICLR conference. \n", "extra_scores": {}, "extra_text": {}}, {"review_id": "O5NhgmcwWw9", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary:\n\nThe paper provides a novel pretrained language model for document understanding named BROS, which adds spatial layout information and new area-masking strategy.\nThe authors do some experiments on four public datasets to illustrate the effectiveness of BROS.\nThe new architecture is well-suited for understanding texts in document, which is valuable.\n\n##########################################################################\n\nReasons for score: \n\n \nOverall, I vote for accepting. \nI deem that the pre-trained language model based on BERT that encodes spatial information is useful for 2D document.\nHopefully the authors can address my concern in the rebuttal period (see cons below). \n\n \n##########################################################################Pros: \n\n \n1. The paper addresses siome limitations which are very important for document understanding: spatial information, spatial\nrelation, and the information of text blocks.\n\n \n2.  This paper provides comprehensive experiments, including both qualitative analysis and quantitative results, to show the effectiveness of the proposed model.  The entire structure is organized well and the formulas are very detailed.\n\n \n##########################################################################\n\nCons: \n\n1. What are the advantages of BROS in terms of speed and resource consumption?\nIt would be more convincing if the authors can provide more cases in the rebuttal period. \n\n2. For the Figure 1(b), it would be better to provide more details about it, which seems not very clear to me. Like how to\nmask in red area?\n\n\n \n##########################################################################\n\nQuestions during rebuttal period: \n\n \nPlease address and clarify the cons above \n\n \n#########################################################################", "extra_scores": {}, "extra_text": {}}, {"review_id": "J_RcevSrOQj", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "> Summary: \n\nThe paper studies the problem of large-scale pre-training for semi-structured documents. It proposes a new pre-training strategy called BERT relying on Spatiality (BROS) with area-masking and utilizes a graph-based decoder to capture the semantic relation between text blocks to alleviate the serialization problem of LayoutLM. \n\nIt points out that LayoutLM fails to fully utilize spatial information of text blocks and will face difficulties when text blocks cannot be easily serialized.  \n\nThe three drawbacks of LayoutLM are listed:\n* X-axis and Y-axis are treated individually with point-specific embedding\n* Pre-training is identical to BERT so does not consider spatial relations between text blocks\n* Suffer from the serialization problem\n\nThe proposed three corresponding methods of BROS are:\n* Continuous 2D positional encoding \n* Area-masking pre-training on 2D language modeling\n* Graph-based decoder for solving EE & EL tasks\n\n> Strength:\n\n* The paper makes incremental advances over past work (LayoutLM) and the proposed BROS models achieves SOTA performance on four EE/EL datasets (i.e., FUNSD, SORIE*, CORD, and SciTSR)\n\n* The paper is generally easy to follow and could be better if provide more important details in Section 3.2 & 3.3\n\n* The experiment and discussion for Section 5.3 are quite convincing. BROS could achieve robust and consistent performances across all the four permuted version datasets, which demonstrates that BROS is adaptive to documents from the practical scenarios. \n\n> Major concerns:\n\n- For Section 3.2, the author didn’t even provide the pre-training objective for the area-masked language model. I think the author should include the objective and define the exponential distribution explicitly.\n\n* I’m confused about why the performance difference in Table 4 between original LayoutLM and BROS is larger than that in Table 1. In the original LayoutLM, the 2D position encoding method is tied with token embedding. This applies to both Table 1 and Table 4. However, in Table 4 the performance difference on FUNSD EE is 42.46 vs 74.44, while in Table 1 the performance comparison is 78.89 vs 81.21. Could the author give some explanations on this?\n\n- In Table 4, it is better for the author to clearly indicate each ablation’s components. The author should also give the performance of the original LayoutLM and performances after gradually adding each new component to the original LayoutLM: such as original LayoutLM + Sinusoid & Linear, original LayoutLM + Sinusoid & Linear + untied with token embedding, etc.\n\n* For encoder design in Eq. (2), the second term is used to model the spatial dependency given the source semantic representation.  How about adding a symmetric term to model the semantic dependency given the source spatial representation. My question is simply that why not adopt a symmetric design for Eq. (2)?\n\n* Can the author give the reason behind the design of $\\mathbf{t}^{ntc}$ in Eq.(4)? I’m not so clear about the function of $\\mathbf{t}^{ntc}$.  Does the $\\mathbf{f}_{ntc}$ output a distribution of the probability to be the next token over all N tokens? \n\n* Could the author give a detailed analysis on which strategy contributes the most to BROS’ robustness against permuted order information? From the results of Table 4, it is not the SPADE decoder and the most important factor seems to be calculating semantic and position attentions separately, i.e., untied with token embedding and explicitly modeling semantic/position relations between text blocks. Do the authors agree with my conjecture?\n\n> Minor concerns:\n\n* Although SPADE is not the most important component of BROS, I believe including details of the grade-based decoder will help the readers to understand the model much better.\n\n* I’m curious about the performance of SpanBERT on the four datasets since the author said that area-masking of BROS is inspired by SpanBERT.\n\n* In Table 3, the value of LayoutLM - FUNSD should be 78.89 since all other p-FUNSD & FUNSD related values are consistent with Table 1 & 2.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes a new pre-trained language model for information extraction on documents. It consists of a new pre-training strategy with area-masking and a new graph-based decoder to capture the relationships between text blocks. Experimental results show better performances of the proposed approach.

Pros • The paper is generally clearly written. • Experimental results show better performances on the benchmark datasets.

Cons • Novelty of the work might not be enough. For example, the graph-based decoder is not new. The masking technique is also a natural extension of that in BERT. • Significance of the work might not be enough. For example, the improvement from the area masking is not so significant. • There are additional experiments which can be added, as pointed out by Reviewer 3. • Presentation can be further improved. Some of the issues indicated by the reviewers have been addressed in the rebuttal. We appreciate the authors’ efforts.

During the rebuttal, the authors have addressed the clarity issues pointed out by the reviewers. However, the main issues in novelty and significance still exist. The reviewers think that the quality of the work is still not high enough as an ICLR paper.


Decision: Reject