Learning to Model Editing Processes
Authors: Machel Reid
Graham Neubig
Venue: ICLR 2022 Submitted (2022)
Keywords: Edit
Representation Learning
Source-code
natural language editing

Abstract: Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced edit-based models for various tasks (such as neural machine translation and text style transfer), but these generally model a single edit. In this work, we propose to model editing processes, modeling the whole process of iteratively generating sequences. We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multi-step edits. We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits.

Under review as a conference paper at ICLR 2022
LEARNING TO MODEL EDITING PROCESSES
Anonymous authors
Paper under double-blind review
ABSTRACT
Most existing sequence generation models produce outputs in pass, usually left-to-right. However, this is in contrast with a more natural approach that hu- mans use in generating content; iterative refinement and editing. Recent work has introduced edit-based models for various tasks (such as neural machine transla- tion and text style transfer), but these generally model single edit. In this work, we propose modeling editing processes, modeling the whole process of iteratively generating sequences. We form a conceptual framework to describe the likeli- hood of multi-step edits, and describe neural models that can learn generative model of sequences based on these multi-step edits. We introduce baseline results and metrics on this task, finding that modeling editing processes improves per- formance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits!!
INTRODUCTION
Revising and editing are a central part of the the human creative worflow, with most original content (e.g. art, books, articles, source code) being developed not in a single iteration, but in many iterations with each more refined than the last. How can we model these editing processes from inception to completion? In this paper, we attempt to provide a first answer to this question, specifically focusing on generation of sequential data such as natural language documents or source code.
Most current work on language generation tasks such as machine translation (Vaswani al. 2017 language modeling (Baevski & Auli 2018). or summarization (See et al. 2017) generates the tar- get sentence or document in single pass (usually from left to right). There has been a reasonable amount of work that can generate edits to existing sequences for the purposes of post-editing, gram- matical error correction (Omelianchuk et al. 2020), text style transfer (Mallinoon et al., 2020 Malmi et al. 2020 Reid & Zhong (0211, ), sentence fusion (Malmi et al. 2019), or machine translation (Gu jet al. 2019). However, these works all 1) model only a single editing step and 2) do not fully define model of incrementally editing document from a blank slate to the final text, and thus do not stand in for the one-pass generative models of sequences described above.
In this context, we propose the task of modeling editing processes in which we look to explicitly model the likelihood the entire process of revising a document to a polished form. In particular, and in contrast to previous works modeling edits, we hypothesize that in order to edit more accu- rately, instead of simply learning to predict the next revision given the current revision, we should have context of multiple previous revisions when deciding when and how edit the document next. Given the novelty of this task, this paper simultancously proposes 1) both baseline and novel mod- els for the task, 2) evaluation methodology, and 3) evaluation datasets that can be used to compare model efficacy.
The proposed multi-step editing model predicts discrete edit operations (Levenshtein 1966) to en- able progressive refinement as shown in Figure [| rather than framing sequence editing as a sequence to sequence task (Reid& Zhong 2021 Faltings et al.) (02T). In the figure, for each step of the edit- ing process discrete operations (insert, replace, delete, keep) are predicted and then actions (such as generating a replaced span) are performed based on this. This has two benefits: 1) it allows the model to scale well with respect to input sequence length, and 2) allows us to make substantial changes with fewer actions (Grangier & Auli 2018). We use these edit operations to condition a semi-autoreerresivve model that is able to insert and replace multiple spans once. Combined with
'll data and code will be open-sourced upon acceptance.

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The
dog
cute
The
dog
animall and friendly
32
The
dog
is an animal and is descended from the wolf
The
domestic
dog
domesticated
desccendant the
wolf
INSERT
DELETE
REPLACE
KEEP
Figure I: An example of a natural editing process based on the description of "Dog" on Wikipedia. The legend below denotes the edit operations for each step of this process.
an encoder that able to quickly specify which spans of text need to be changed and how, this al- lows for considerable changes to be made to the text (including insertion, deletion, re-ordering, and replacement) relatively simple and cheap manner. Furthermore, this allows us to disentangle how likely the model is to operate (replace, delete, etc...) on a given span, and how likely the model thinks the generated text for a given span is. As we are modeling editing processes, and hypothesize the sequence are helpful,
that context from edits applied sequence compression which can compress sequences into their edit operations and use relative edit positional embeddings specify the position of edits relative each other.
propose method for edit-aware
To evaluate the efficacy of proposed and baseline models, we propose an evaluation metric, edit perplexity (ePPL), which combines both of these ideas for unified metric measure edit like- lihood. However we can easily disentangle this into operation perplexity (oPPL) and generative perplexity (gPPL) which measures the likelihood of performing a given operation on a span, versus the likelihood of newly generated text for the next revision.
Given that the task of modeling natural editing processes itself is novel, we collect new datasets to study this behavior; WIKIREVISIONS and CODEREVISIONS. These datasets, in the code and natural language domains respectively, cover over 2.5M and 2.3M natural sequential revisions.
We train and evaluate our proposed models on these datasets. Our experimental results demonstrate that edit perplexity, generative perplexity, and operation perplexity are all reduced when one mod- els edits with previous changes/edits as context. We also qualitatively demonstrate the ability of model to generate natural edit sequences, and demonstrate the utility of the learned representa- tions downstream tasks of commit message generation (Loyola et al. 2017) and edit intention classification Yang et al. 2017).
2 PROBLEM DEFINITION
Let X (2o. ...... Ex be a series of N versions of a document, where the ith revised document denoted by z; zo represents an initial state (generally the null string), and æx represents the current state of the edited document. The probability of this series of document versions occurring can be decomposed as
N
(XX
II (
where wh 1 := ..,...,,; (similarly below). The right hand side is the likelihood of the transfor- mation of the previous document version æ;-1 the current document version æ; given the previous revision history &zg. We refer to the likelihood of the whole revision process as the edit likelihood, and judge learned models based on their ability achieve high edit likelihood held-out data.
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Note that standard generative models (specifically language models; LMs) calculate the probability of only the final version p(æx), whereas the proposed formulation calculates the probability of the entire sequence of document edits. It nonetheless could be used to calculate the final version's ss likelihood by treating the editing process as latent and marginalizing over it
p(aN))
Σ
(X)).
(8 BN
Thus our formulation, in contrast previous single-step models of edits (Yin et al. 2019b; Malmi et al. 2019; Reid & Zhongg 2021), can also be used to define generative model over single doc- uments. It is also worth noting that the final document likelihood is lower-bounded by the edit likelihood; i.e. p(æN) 2 p(X).
3 MODELING EDITING PROCESSES
In this section, we now describe our approach to actually modeling these sequences of edits through (I) a decomposition of the modeling process into a sequential process of modeling edit operations then actual edits, and (2) neural model of modeling these operations and edits.
3.1 MODELING OPERATIONS AND CERRAATON-CONDITIONN EDITS
While the probability p(z;|1z ( 1 of the next document given all previous document versions could theoretically be modeled with single neural sequence model, this infeasible computationally (and likely infeasible from learning perspective as well). To simplify this problem, we employ the n-th order Markov assumption, assuming that the probability of the next document is conditioned only on the previous n documents p< This probability could be modeled directly, and in fact in the case of = 1 this becomes analogous to the single-step editing problem tackled by previous work (Yin et al.) 2019b Malmi et al. 2019) Reid & Zhong 2021, 2021, Faltings et al. 202T). To 1.
our knowledge, no previous work has modeled natural editing processes with
However, in the interest of both efficiency and efficacy, we take an alternative approach where we first predict set of edit operations e;, and then predict the next document version based on the previous documents and these edit operations:
p(x+|x==) = p(x, xx(x)=
(3)
-, x
(4)
The first approximation becomes an equality when the edit operations can be deterministically de- rived from x; and X;-1, i.e. p(e; |x,, x;-1) 1, is the case described below.
Edit Operations. We base the edit operations in those calculated by the Levenshtein algorithm [evenshtein [966), including token-level insertions, deletions, and substitutions. These are ex- pressed four operations insert, delete, keep, and replace denoted by (INSERT, DELETE, KEEP, REPLACE). For multi-word insertions and replacements, e.g. a replacement of a contiguous span of words, we apply the the same REPLACE label to all tokens in this span. An example of each operation is shown in Figure I
Decomposed Edit Likelihood. We can then re-define our previous formulation of edit likelihood:
N II p(xlee
P(xN)
(5)
1=1
and analogously define edit log-like ligeliilldood
Lxx = log P(xM) = Σ logp(x, x(-]) + log logp(es
(6)
We can further decompose this into only the components corresponding to the edit operations L. = EN log p(e, xx[)), or the operation-conditione edits Lxle == N
i= log p(x; je;, x(=)), both of
which we will utilize for devising evaluation metrics in Section 5.2 below.
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domestic
damesicalla descandant
Decoder
The
dog
is
animal
and
descended
from
Zio
welf
INGERT
KEEP
KEEP
IXEL
DEL
DEL
DEL
REPL
REPL
KEEP
KEEP
Autoregressive Predictor
Encoder
dog
descended
she
compresded
Figure 2: EDITPRO given the examples of modeling p(a, 1) from Figure 1. We feed the input tokens into encoder with autoregressive tag predictor, and then use the predicted edit operations to condition the generation of REPIACE and INSERT spans.
3.2 EDITPRO
In this section, we propose model of multi-step editing processes, EDITPRO, which is based on semi-autoregressive edit-conditioned encoder-decoder model with Transformer (Vaswani al., 2017]. The model (depicted in Figure() contains three main components: (1) an edit encoder, (2) an operation classifier and (3) an insertion-replacement decoder.
Edit Encoder. The encoder fene takes in document version #;-1 and feeds it through multiple self-attention and feedforward layers (Vaswani et al. (017) to produce contextual representations for each token. In the case that perform variable-order edit modeling, we use cross-attention to feed in representations of previous edit steps. For models where 1, we feed in n 1 additional edit sequences we describe this process after describing our methods for edit sequence prediction.
autoregressive tagger, using a single Transformer layer with a causal attention mask, that models the probability of cach edit in edit operation sequence e:M from left to right, p(ez l Notably, we also performed preliminary experiments with a tagger that predicts operations independently, but found was heavily biased towards the KEEP operation most words are kept in any single document revision, and thus did not produce coherent multi-word edit sequences when sampling sequences of edits.
Edit Operation Prediction. We
Generating Replacements and Insertions. When editing, given our four Levenshtein operations (INSERT, REPLACE, KEEP, DELETE), two of them INSERT and REPLACE entail generation of new content conditioned on the current revision of the document. Given our predicted edit operations we propose a cemi-autoregressive model with a Transformer decoder that can decode multiple spans in parallel for efficiency purposes. Each edit span contains the following properties: it has a start index (denoted by saun),, end index (denoted Send), and operation type (denoted by styp)
Note that these can be simply be extracted by looking at contiguous spans of a certain type in an edit (e.g. REPLACE for descended from domesticated descendant of in Figure We mean pooling operation to aggregate the contextual vectors produced by Seno into span representation f.
Rext
8,
fenc (x))
Send
Sstart
We then update the span representation å, by taking the sum the appropriate operation embedding for the span type and the current span representation and feed it to multi-layer perceptron with an intermediate non-linearity: &, +- MLP (Wap (e), +.+). where Wop denotes an embedding matrix for each operation. f. is then used to initialize the 
 token for the decoder span further condition the generative process.
Encoding Edit History. As we look to investigate variable order edit modeling over long sequences of text, need way to be able to represent edits in way useful for predicting the next editing steps. Previous work (Yin et al. 20199; Marrese- Taylor et al. 2021 Yao et al., (02T) has focused largely learning single-vector representation for edits which is compressed but limited in expres-
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Dataset
Num. Edits
Avg. Len (Max/Min)
Keep
% Insert
% Replace
% Delete
WIKIREVISIONS
2.5M
333 (9992/1)
82.4%
0.1 %
8.7% 11.3%
8.8%
CODEREVISIONS
2.3M
774 (9725/1)
76.9%
0.1 %
11.7%
Table 1: Dataset statistics on CODERBVISIONS and WIKIREVISIONS, average length is measured by whitespace tokenization
siveness. One the other hand, perhaps more intuitive way taken from common Transformer-bass (Vaswani et al. 2017) models would be to use cross-attention between all n previous documents, which is more expressive but prohibitively expensive when n is scaled upwards.
Instead, we make a compromise between the above approaches, leveraging predicted edits e to compress the sequence and their derived spans (as discussed above). Given each of these spans, we compute the edit-compressed sequence, composed of sequence of vector representations with each vector representing different span. For each span in each of the previous revisions in w;=1), we mean pool the encoder (pre-edit) and the decoder (post-edit) representations for that span. We then sum this representation with the operation representing its edit operation and feed it into an MLP. Once we have done this for each span, we sum a learned relative edit positional embedding. where we learn embedding matrix where each index in the matrix represents each order. This is to specify the order of the previous edits. Finally, we compose these into a sequence and treat that as the "edit-compressed" sequence representation for that edit.
Turning Pre-trained Encoder-Decoder Models into Editors. Despite the fact that our model introduces both an edit prediction and a serreevtoeeeressive component, is easy to finetune pre- trained language model into an editor with our method it uses vanilla Transformer layers backbone. We perform this by batching various spans and their conditioning variables together and training the model to adapt to decode these in parallel.
DATA
While some datasets of edits exist (Faruqui et al.) 2018: Marrese-Tayllor et al. 202T), to our knowl- edge they only consider single isolated edit, i.c. dealing with document X { 3, 1. As we propose learning to model multi-step edits, we develop new datasets in both the code and nat- ural language domains. In addition, previous datasets have only concerned themselves with atomic edits (Faruqui et al., 2018) which only occur at small scale (usually sentence-level), and we in- stead look to model scrger-scale edits as document level changes, which more representative of the natural editing process.
4.1 WIKIREVISIONS
For the natural language domain of Wikipedia, we collect data for each revision using dumps from English Wikipedia. Given that the dumps are provided in the XML format, we extract the text with beaut 1 suls and remove wikitext (custom Wikipedia markup) with wi With this sanitized data, we gather revision of each document in chronological order removing any metadata-based edits which were stripped of revisions we tokenize all text with the sentencepiece model used by RRafford et al. (2018): Liu al. (2019) for congruence with pre-trained models (see Section 3.2). We pre-compute Levenshtein operations using pytthonLevv Lene-LLeeesseeeeee exceeds 2000 tokens, split the articles into its subsections and treat each subsection (for the purpose of modeling editing processes). Dataset statistics are shown in Table [ We note that there is significant imbalance for the INSERT operation, this because define insertions to be applied to the token preceding the insertion (as shown in Figure []] rather than applied to an entire span (as we do for the deletion, replacement, and keep operations).
result of the sanitization process. Now, with our sets
for use during training. In the case that an article
article
Edit Summaries. When extracting each edit keep the edit summary (akin commit message) supplied by the editor at time of editing. We then curate these comments and develop a dataset
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for usage downstream tasks for both edit summary generation (oyyola et al. 2017) and edit- summary-connitionee text editing (Faltings et al. 2021).
4.2 CODEREVISIONS
When buildling CODEREVISIONS, we scrape total of 700 Python GitHub repositories using the MIT License with at least 1000 commits and 500 stars. We extract line-level patches from each repository's ' commit history when forming our code-based corpus and progressively apply each patch and compute the token-level Levenshtien operations between each revision. Note that we also keep commit messages for each commit. For this dataset we operate on the file level. For each series of visions, we precompute Levenshtein operations based on tokens derived from sent eepeepiicee (Kudo Richardson] 2018) model with a 10k vocabulary. We also curate a dataset revisions with commit messages described in the previous subsection.
5
EXPERIMENTAL SETUP
5.1 BASELINES
We use the following baseline for our edit modeling task: (1) LEWIS (Reid Zhong 202I), ofate-of-the--rt single-step editing model, which uses separate encoder-only tagger and sequence- to vo-sequence generator setup during training.
5.2 METRICS
Many previous works on editing (Malmi et 2019 Gu al.) 2019 Reid Zhong 2021 have used non-likelihood based metrics such as BLEU or Fl, representing the downstream tasks which they were trained for, however as we look model the intrinstic editing process (i.e. applying and generating sequence of edits) and the likelihood of performing certain edits, we propose the following metrics. Note that |x] refers the token count for the newly generated/inserted spans, and |e| refers to the number of edit operations:
Edit Perplexity (ePPI.) is the exponent of the negative log likelihood for both the edit operations and generated outputs, divided by the length of both sequences, exp(
-Oxe.)
x
Generation Perplexity (gPPL) measures the likelihood of generating replaced or inserted spans when compared with the ground truth edit sequence as follows exp(
-xje
[x]
Operation Perplexity (oPPL) exp( 76-) is the likelihood of predicting set of edit operations.
5.3 TRAINING SETUP
We train our models using the Transformer implementation in the HuggingFace (Wolf et al. 2020) library. We tokenize data using SentencePiece (Kudo & Richardson 2018), using the same vocabu- lary employed in Liu et al. (2019); Lewis et al. (2019) for natural language, and using a custom 10k subword vocabulary for code. We use the Transformer architecture of hidden dimension feed- forward size of 3072, and 6 layers for both the encoder and decoder. We initialize all natu- ral language models with BART (Lewis et al., (2019), and code models with random initialization. We the maximum sequence length for all models to be 2048, using batch size of 65K tokens distributed over 8 A100 GPUs.
768,
5.4 DOWNSTREAM TASKS
In addition assessing our proposed model's generative capacity, we assess the quality of the leamed representations on downstream tasks:
Conditional Editing We also continue training using the commit messages gathered during the cleaning process as a conditioning variable, essentially reformulating our p(x)|=(-1) to p(æ;//=/_1 c) to add the additional conditional variable which set to be the edit summary or commit message in this setting. With our model, we append the comment to each document, delimiting with separator token 
 follows: DOCUMENT 
 COMMENT.

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Edit-conditioned Generation. We define edit-conditioned generation to be tasks which rely on intermediate edit representations of documents to generate text describing the changes in text, sim- ilar to that proposed by Loyola et al. (2017) for source-code commit message generation. As aim to determine whether the information contained about the edit itself is more informative as we add additional context, we condition the generation solely on the edit-compressed representations of the last edit step. To accomplish this, we use randomly initialized Transformer decoder with cross-attention on these edit-compressed representations.
Edit-conditioned Classification. In the natural language domain, also test our representations on an edit-oriented classification task, namely semantic intent classification (Yang et al., 2017) In Yang et al. (2017), they classify 5,777 Wikipedia revisions into 10 intention classes, such as "Clarification" "Vandalism". and others with each representing different intention. We form splits of 4,601 train examples, 588 valid examples, and 588 test examples Similarly to our setup for edit-conditioned generation, we also test our classifier (consisting of a self-attentive span extractor (Lee et al. 2017) and multi-layer perceptron) on this task.
RESULTS
6.1
EDIT MODELING
Results on edit modeling for both CODEREVISIONS and WIKIREVISIONS can be seen in Table 2 where we measure edit perplexity, operation perplexity, and generative perplexity, We first note that our model significantly outperforms LEWIS (Reid & Zhong, 2021) on WIKIREVISIONS, by 8.6 ePPL. suggesting that our model formulation is superior at this task. We believe that this stems explicitly
from the fact that our model is trained to explicitly generate the newly added spans, and because it directly connects the operation prediction and generation processes. We also take note that ePPL decreases when the order of context increases. In particular, we take note of the significant gain when introducing the notion of editing processes to the model in our 2-order setting (in contrast to a single edit step), with a reduction of 3.4 ePPL on natural language and 4.4 ePPL on source code.
We also note that while the gPPL consistently decreases as the number of orders increases, opera- tion perplexity does not perform as consistently. Interestingly, we find that single-order models tend to be more confident with respect to keeping tokens (the overwhemingly dominant edit operation), while other operations (deletions, replacements and insertions) are not predicted as well. In con- trast, learning higher-order editing processes almost universally decreases the oPPL for non-KEEP operations, indicating the necessity to model longer context to capture these rarer edit operations.
Likely and Unlikely Edits. We perform qualitative analysis on subsample 4,000 natural language edits I examining which edits are judged to be likely (or unlikely) and with respect to which metrics. We do this by identifying outlier values for each metric (significantly above or below the average) and further analysing these for unique properties.
As result, we found that many of the edits with higher oPPL. were spam and vandalism-type edits, as many of the edit operations have more of a random nature. However notice that generative perplexity was much lower as these edits tend to be repetitive in nature with the same ngrams of- ten being repeated for long spans. However, we notice that, irrespective of the number of orders, when editing reverted spam-like content, the oPPL for the REPLACE and DELETE operations are extremely low (on average 1.07 and 4.4 respectively). The importance of variable-order model- ing was particularly evident these revisions where the gPPL in the single-order setting averages at 123.90 gPPL,, however when using 2-orders edit-compressed sequences provide useful context about the previous revisions. We also notice that models are able predict insertions (2.25 INSERT oPPL) significantly better when they come after the end of sentence, representative of many insertions in Wikipedia. We also notice that outside of the above settings, models with extra context generally predict more likely edits supporting the notion of modeling edit processes compared to modeling changes individually.
able to attain 67.83 gPPL indicating that the
1 We contacted the authors, but they were no longer able to release the folds they used in their K-fold cross- validation setup, hence our creation of splits A precursory examination found these to he more interpretable than code edits.
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oPPL
DATASET
Model
ePPL
gPPL
DEL
KEEP
REPL L
INS
LEWIS
65.94
48.85
24.29
1.09
19.49
507.76
EDITPRO (1-order)
57.32
42.43
25.53
1.09
18.36
1826.21
WIKIREVISIONS
EDITPRO (2-order)
53.91
39.87
20.70
1.13
15.49
376.15
EDITPRO (3-order)
50.84
37.66
19.30
1.13
14.88
252.14
EDITPRO (1-order)
34.22
28.02
125.21
1.05
10.38
544,57
CODEREVISIONS
EDITPRO (2-order)
30.85
26.26
84.77
1.05
9.30
304,90
EDITPRO (3-order)
29,47
25.37
75.19
1.06
8.16
441.42
Table 2: Results on Edit Modeling
DATASET
Model
BLEU
F1
ePPL (A)
EDITPRO (1-order)
10.7
57.8
54.72 (-2.60)
WIKIREVISIONS
EDITPRO (2-order)
11.3
61.3
51.83 (-2.08)
EDITPRO (3-order)
11.6
61.2
49.91 (-0.93)
EDITPRO (1-order)
13.8
33.65 (-0.57)
CODEREVISIONS
EDITPRO (2-order)
14.3
30.13 (-0.72)
EDITPRO (3-order)
14.5
29.08 (-0.39)
Table 3: Results on Edit Generation (BLEU), Edit Classification (measured with micro-Fl), and Conditional Edit Generation (measured Edit Perplexity = ePPL). Note that the A symbol refers to the change between the model's non-message conditioned version Table 2
6.2 DOWNSTREAM PERFORMANCE
Results conditional edit generation, edit classification and edit citiiiconiioneed generation can be seen in Table 3 We note that findings generally follow the edit modeling results, with additional context improving performance further giving supporting evidence to modeling editing processes. Specifically, increasing context from single-order to 3-order improves commit message generation performance by 1.9 and 0.7 BLEU for both natural language and source code respectively. We also note that ePPL decreases similarly when we add natural language conditioning to the editing process, which indicates that multi-order editing encodes fine-grained information not found in the commit message.
Edit Modeling In particular, when performing editing using an editor pre-trained on edit modeling, note that when sampling from the autoregressive tagger it almost always predicts KEEP with extremely high confidence given the overwhelming class majority. We instead perform a variety of posterior regularisation (Ganchev al.). reducing the probability of the KEEP class until the sampled edit labels to grow closer in proportion to the true distribution of edit operations (Table Combined with this technique, we are able generate more diverse edits, which we show in Table
Semantic Coherence In looking the example generations in Table 4 we note that the generated text is not perfectly semantically coherent, despite showing topical coherence and some degree of discourse coherence. We believe this is largely due the size the language model we use, being trained solely on Wikipedia data (which contains a variety of minor edits including spam/vandalism) Given this, we expect improved semantic coherence upon scaling up data, data diversity and model scale. However, we note the improved aaneext-wwareness of the edit path shown by the 2-order model over the 1-order model, providing qualitative evidence for modeling editing processes and looking at different forms of document construction.
7 RELATED WORK
Learning Edit Representations. Previous work on learning properties inherent to editing has largely focused on learning distributed representations edits. Yin et al. (2019a) proposed this task, using attentional ceoueeccett-scquenee model learn representations. Marrese- TaylerTyloorrroo al. (202T) expands upon this approach, introducing an auxiliary loss and a variational setup. More re- cently, Yao al. (202T worked using this approach for editing tree-structured data. However, in
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Initial Sentence (1-order)
Europe is continent located entirely in the Northern Hemisphere and mostly in the Eastern Hemisphere.
22
Europe continent located entirely in the Northern Hemisphere and mostly in the Eastern Hemisphere. Spain member of the European Union.
473
Europe is a continent located entirely in the Northern Hemisphere and mostly in the Eastern Hemisphere. France member the Europcan Union.
Europe a continent located entirely in the Northern Hemisphere and mostly in the
Eastern Hemisphere. France is is a lieing country in the world. It is a bunch of crap.
23
Europe continent located entirely in the Northem Hemisphere and mostly the
Eastern Hemisphere. Prance Heing country the world. i- bunch -feap- There is a type of dehate of group of people who are not considered to be part of the United Nations.
Initial Sentence (2-order)
Europe continent located entirely in the Northern Hemisphere and mostly in the Eastern Hemisphere.
422
Europe a continent located entirely in the Northern Hemisphere and mostly the Eastern Hemisphere. The Western South Eastman Islands are also located Europe.
Europe is j...a.jf..ooskkkkls.
274
Europe ff a continent in the Northern Hemisphere. The Islands also in Europe and they are great.
Table 4: Example generation when sampling with an edit model. We notice that the 2nd order model is able perform a revert operation given the context fed through the edit-compressed sequence about the previous revision, whereas the l-order model although deleting its generated spam, generates something relatively unrelated. However we note that this reversion is not exact (likely due to the information loss during edit compression). This corresponds with our observations in our qualitative study (where likelihood of reverted edits is increased in the 2+ order models).
contrast with this work, these approaches only consider modeling single editing steps instead of the more general multi-step setting tackled here.
Semi-Auutorgrresiive Sequence Generation. Work in machine translation has explored non- autoregressive methods that use an iterative generation process. This was first proposed by Lee it al. (2018) using latent variables and denoising autoencoding. Iterative refinement was then ex- plored with conditional masked language models (CMLM: (Ghazvininejad et al., 2019). simplifying previously proposed methods, by iteratively replacing predicted tokens with low confidence. Gu al. (2019), introduced the Levenshtein Transformer, making this approach more flexible by intro- ducing insertion and deletion operations. However, these methods have not yet considered modeling natural editing processes, instead using either random sampling or heuristically determined orders.
Other Editing-based Work. Other work on editing has included editing for sentence fusion (Malmi cet al. 2019), in which one can perform minimal operations to join two sentences together grammat- ically. Furthermore, with text style transfer in which the difference between sentences in different styles (e.g. positive negative) 2020). Furthermore, Falttrn et al. 2021) explored natural language conditioned editing as a means for controllable text generation using a T5-based (Raffel al. 2020) sequence to sequence model. Also related to our work is text morphing (Huang et al.) 20188), in which they look at an edit-based model to interpolate between two sentences. We also note that diffusion models (Sohl-Dickstein et al., 2015 Ho et al. (2020) can be formulated as a flavor of editing models, where the model learns to iteratively edit some representation of information in order to construct final version of said representation.
be relatively minimal (Reid & Zhong 2021; Mallinson et al.)
8 CONCLUSIONS
In this work, we proposed the novel task of modeling editing processes, in which we model the likelihood of documents by way of their natural editing processes. We develop new datasets and curate existing datasets for downstream tasks. We find that modeling editing processes is benefi- cial to this end, in contrast to modeling single-order edits, as has been done in much of previous literature. More broadly, we believe that tackling iterative refinement tasks in broader NLP may be beneficial given its resemblance to the natural generative and creative process. In future work, we look to investigate methods for transferring pre-trained edit models to wider range of NLP tasks.
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ETHICS STATEMENT
This work has impact in the fields of natural language processing and machine learning, and pro- poses new task of modeling editing processes, while proposing new datasets and models this end. There are several potential ethical concerns. First, we initialize our models with pre- rreitraind model (trained on webtext), which may capture and amplify biases found in the pre-training data. We further fine tune natural data which, in addition to benign edits, also contains a portion of human-gemnnnee -enearrtee spam and potentially offensive edits (especially which may also amplify and capture biases in that regard. On the other hand, we have also demon- strated the ability of the proposed model to identify and revert adverse edits performed by spammers malicious editors, potentially useful tool for content moderation, even detecting automatically generated spam edits, as has been done in the detection fake news (Zellers et al. 2019).
the natural language case),
REPRODUCIBILITY STATEMENT
As noted on the first page. all code and datasets necessary to reproduce the experiments in this paper will be released upon conclusion of paper review. Furthermore, all data processing steps are discussed in Section 4 and Appendix A.1 for both code and natural language.
REFERENCES
Alexei Baevski and Michael Auli. Adaptive Input Representations for Neural Language Modeling. In International Conference on Learning Representations, September 2018.
Felix Faltings, Michel Galley, Gerold Hintz, Chris Brockett, Chris Quirk, Jianfeng Gao, and Bill Dolan. Text editing by command. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5259-5274, Online, June 2021. Association for Computational Linguistics. doi: 18653vv// 2021 www.aalallaaaaam main.aaii444444
URL https: htttpsss//ccaaancccloyyy/ hwtp://hoooooyooo//222221122111 maallooo.....o.222mmmmaaaacmmaa.
414
Manaal Faruqui, Ellie Pavlick, Ian Tenney, and Dipanjan Das. WikiAtomicEdiis: A Multilin- gual Corpus of Wikipedia Edits for Modeling Language and Discourse. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 305-315, Brus- sels. Belgium, 2018. Association for Computational Linguistics. UtLhttp::/aclwebb. Sothoooogy/DBB-100
Kuzman Ganchev, João Graça, Jennifer Gillenwater, and Ben Taskar. Posterior Regularization for Structured Latent Variable Models. pp. 49.
Marjan Ghazvininejad, Omer Levy, Yinhan Liu, and Luke Zettlemoyer. Mask-predict: Parallel decoding of conditional masked language models, 2019.
David Grangier and Michael Auli. Quickedit: Editing text & translations by crossing words out, 2018.
Jiatao Gu, Changhan Wang, and Jake Zhao. Levenshtein Transformer. arXiv:1905.11006 [cs), October 2019.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models, 2020.
Shaohan Huang, Yu Wu, Furu Wei, and Ming Zhou. Text morphing, 2018.
Taku Kudo and John Richardson. Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing, 2018.
Jason Lee, Elman Mansimov, and Kyunghyun Cho. Deterministic non-autoregressive neural se-
quence modeling by iterative refinement, 2018.
Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. End-to-end Neural Coreference Res- olution. arXiv: 707007045 [cs], December 2017.
10

---

Under review as a conference paper at ICLR 2022
V. I. Levenshtein. Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady, 10:707, February 1966.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy. Ves Stoyanov, and Luke Zettlemoyer. BART: Denoising Sequence-to-Sequenae Pre- training for Natural Language Generation, Translation, and Comprehension. arXiv:1910.13461 [cs, stat], October 2019.
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. RoBERTa: A Robustly Optimized BERT Pre- training Approach. arXiv: /90p:1997..19999 [es], July 2019.
Pablo Loyola, Edison Marrese-Taylor and Yutaka Matsuo. A neural architecture for generating natural language descriptions from source code changes. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 287- 292. Vancouver, Canada, July 2017. Association for Computational Linguistics. URL http: wwt.b.orggaattoooorr//--------2.
Jonathan Mallinson, Aliaksei Severyn, Eric Malmi, and Guillermo Garrido. Felix: Flexible Text Editing Through Tagging and Insertion. arXiv:2003. 10687 [cs], March 2020.
Eric Malmi, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, and Aliaksei Severyn. Encode, Tag, Realize: High-Precision Text Editing. In Proceedings of the 2019 Conference on Empirical Methwds in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5054-5065, Hong Kong, China, November 2019. Association for Computational Linguistics. doi: 10. 10.18663/v1/D11-155000
Eric Malmi, Aliaksei Severyn, and Sascha Rothe. Unsupervised Text Style Transfer with Padded Masked Language Models. In Proceedings of the 2020 Conference Empirical Methods in Natural Language Processing (EMNLP), pp. 8671-8680, Online, November 2020, Association for Computational Linguistics. doi: 10. (0.18653/+122020eemmll) mannlp-miii.699
Edison Manrese-Taylor Machel Reid, and Yutaka Matsuo. Variational inference for learning repre- sentations of natural language edits. AAAI, 2021.
Kostiantyn Omelianchuk, Vitaliy Atrasevych, Artem Chernodub, and Oleksandr Skurzhanskyi. Gee- tor grammatical error correction: Tag, not rewrite, 2020.
Alec Radford, Karthik Narasimhan. Tim Salimans, and Ilya Sutskever. Improving Language Under- standing by Generative Pre- Trainngnn pp. 12, 2018.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the Limits of Transfer Learning with a Unified Text-to- Text Transformer. arXiv:1910. 10683 [cs, stat), July 2020.
Machel Reid and Victor Zhong. LEWIS: Levenshtein editing for unsupervised text style trans- fer. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 3932-3944, Online, August 2021. Association for Computational Linguisties. URL https:
//aclanthology: www.lolyyo.ggg////o 2021. findings-acl. 344
Abigail See, Peter Liu, and Christopher D. Manning. Get To The Point: Summarization with Pointer-Generater Networks. April 2017.
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsuper- vised learning using nonequilibrium thermodynamics, 2015.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All you Need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (eds.), Advances in Neural Information Processing Systems 30, pp. 5998 6008. Curran Associates, Inc., 2017.
11

---

Under review as a conference paper at ICLR 2022
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jemite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. Transformers: State-of-the-ard natural language processing. In Proceedings of the 2020 Conference
Empirical Methods in Natu- ral Language Processing: System Demonstrations, pp. 38-45, Online, October 2020. Associ- ation for Computational Linguistics. doi: 10.18653/v 1t533//12222.ecrm htt.2/00mmmmmdeemoo...... URL https:
//aclanthology 2020. emn emnip-demoss
Diyi Yang, Aaron Halfaker, Robert Kraut, and Eduard Hovy. Identifying semantic edit in-
tentions from revisions in Wikipedia. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2000-2010, Copenhagen, Denmark, Septem- ber 2017. Association for Computational Linguistics. doi: 10.18653/1/D17--111.. URL
Ziyu Yao, Frank F. Xu, Pengcheng Yin, Huan Sun, and Graham Neubig. Learning structural edits via incremental tree transformations. In International Conference on Learning Representations, 2021. /ttpa/////////oo......./////////
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L., Gaunt. Learning to represent edits, 2019a.
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Mare Brockschmidt, and Alexander L. Gaunt. Learning to Represent Edits. In Proceedings of the 7th International Conference on Learning Representations, 2019b. TRR.https: http:p::///peerevieeee net/ wwt.//oormmm/oom
(irrummiidBJ1111 dd=BJ1 6A C5F7
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk. Ali Farhadi, Franziska Roesner, and Yejin Choi. Defending against neural fake news. arXiv preprint arXiv: 2019.
A APPENDIX
A.1 FURTHER TRAINING DETAILS
When training, employ data sharding enable cheaper, the fly data processing. We shard each documents' into shards and form splits based on these shards. Our train-valid-test splits split 90%,5%,5% for commit message generation, commit-conditioned edit modeling, and edit modeling. We use a dropout value of 0.3 and use the GELU activation for all MLPs. We use a learning rate of le-4 warmed up for 1000 iterations. We also note that extended the positional embedding matrix for BART to handle longer sequences.
Eatrccion
Conmit messages other matadata
Approll marching
H
16:001111 GEaling
feoens Wose fiilie guee
an-coeo Janlon
Wikipodia Dumps
Sanitization
Revisions
Figure 3: An overview of the WIKIREVISIONS data generation process for collecting clean multi- step revision data.
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A.2 ABLATION STUDY ON THE IMPACT COMMENTS ON COMMENT-CONDITIONE EDIT MODELING
Model
cPPL (A compared 2-order)
EDITPRO (2-order)
53.91 (-)
EDITPRO (2-order, only current revision is comment conditioned)
52.06 (-1.85)
EDITPRO (2-order, completely comment--onditiooee))
51.83 (-2.08)
EDITPRO (3-order)
50.84 (-3.07)
Table 5: Ablation on the impact of comments in 3-order edit modeling
We include additional experiment, demonstrating the impact of including comment information in previous revisions, and not including comment information in comment conditioned edit modeling. We do this by including 2-order edit model trained with only the current revision being comment- conditioned. The results demonstrate that a majority the performance gain is indeed derived from modeling an extra revision, with the clear outperformance of 3-order edit modeling versus 2-order edit modeling. We also note that there is some gain that is derived from modeling previous revisions also comment conditioned, however we note that this is relatively minimal compared to the comment conditioning of the current revision and modeling an extra order.
13


Reviews:
[{"review_id": "xjw9FFpgYpq", "reviewer": "Reviewer_DCPz", "summary": "", "questions": "", "limitations": "", "rating": 5, "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": "The outset of the paper is valid: Conditioning on the complete (or longer) revision history can potentially model the editing process of documents more accurately than models that just take the latest history into account. However, I'm not completely convinced by the evaluation. I think that it is hard to justify an approach with perplexity improvements only. Perplexity can be a valuable tool to get a better understanding of the system, but ultimately showing the usefulness in a downstream task can make a much stronger case. The two downstream tasks in the paper that do not use perplexity (Tab.3 / Sec. 6.2) are (a) predicting code commit messages / Wikipedia edit comments and (b) semantic intent classification. If I understand correctly, both of these tasks actually do not require the *prediction* of the document edits. Tab. 3 does not compare with related work on these tasks. BLEU and F1 scores do improve with increasing order which is a good sign, but simple baselines/ablations would strengthen the results. For example, task (a) may simply benefit from having access to previous commit messages, not from a better modeling of the edits. Similarly, for (b) the model could learn that two subsequent \"Vandalism\" revisions are unlikely without using edits from previous revisions.\n\nMinor comments:\n- The paper sometimes claims that previous models are just able to represent single *edits* (abstract, Sec. 4). This is, of course, not true: they are able to represent multiple edits with a document, just in a single *edit step*. This subtle difference is quite important so the wording should be more precise.\n- Eq. 2: This might be true in theory, but it is highly impractical since it seems unlikely that the sum over all document revision histories can be approximated to a reasonable degree, let alone enumerated. \n- Eq. 2: The notation under the sigma is wrong. It should start with \\tilde{X}\\in\\{...\\}\n- Sec. 2: \"x_0 represents [...] generally the null string\". Is this true for both WikiRevisions and CodeRevisions tasks? Doesn't this significantly increase the length of the edit representations for subsequent revisions? x_0 is not empty in Fig. 1.\n", "summary_of_the_paper": "Existing edit-based models condition on a single sentence or document. This paper argues that such frameworks do not represent processes well in which the same document undergoes multiple iterations of revisions, for example the edit histories of Wikipedia articles or source code. The proposed system is a two-stage model that first labels each token in the next revision with one of four edit labels, and then employs a seq2seq second stage model to predict the output tokens for generative labels such as INSERT and REPLACE. Both stages do not only condition on the previous revision but on the n latest revisions. Gains in perplexity are shown, as well as improvements of two downstream tasks: \"edit-conditioned generation\" (generating meta information to edits such as comments) and \"edit-conditioned classification\" (1-way semantic intent classification).", "summary_of_the_review": "While there are some potential merits to the idea of modeling multiple revisions, I have doubts whether the evaluation demonstrates them in a convincing way.\n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "gmZXIdAjE2R", "reviewer": "Reviewer_vM6c", "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": "Strong points of the paper:\n1. Previous approaches only consider predicting editing based on the current text (therefore just one revision history). This paper considers more revision to predict and it show more revisions (up to three) are beneficial. \n2. The paper prepares two corpus with full revision history, which might be beneficial for the community on future research. \n3. The paper propose a modified semi-autoregressive Transformer model to predict both the editing operator and generator for insertion and replacement operations. The model is reasonable. \n4. The paper shows improvement on the metrics on WikiRevisions dataset. \n\nWeak points of the paper:\n1. One of the metrics proposed in the paper is slightly strange. Why to measure the perplexity of operation? Why not measuring the accuracy or F1 score of the per-token (excluding the no-edit operation). \n2. There are some details missing about the method in Section 3.2. What is the network for autoregressive operation prediction $P(e_j | e_1^{j-1})$? What is the decoder for insertion and replacement? Is it Transformer decoder? \n3. Examples in Table 4 actually show that this EditPro model generates rather bad text. Those text are fluent but semantically incoherent. \n4. continue from the above point, Human evaluation of the editing quality is missing. Do you conduct human evaluation of the generated text? \n5. It seems a rather simple baseline is missing. If you directly use Transformer encoder-decoder to predict both the operation and the generation text, how is EditPro compare to this baseline?\n\nIt could be a good paper if the authors could fix some major concerns above. \n\nSome minor issue: \nIt seems the perplexity equations are incorrect, missing negative sign. \n\nThere are also additional related work on editing based method for text generation. [1] uses simulated annealing to search for edit operations, and [2] uses Metropolis-Hastings sampling for editing. Do those apply to the edit generation problem here? \n\n[1]: Unsupervised Text Generation by Learning from Search. 2020.\n\n[2]: CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling. 2019", "summary_of_the_paper": "The paper presents a new problem of modelling the series editing operations of an article, i.e. $p(x_1 \\dots x_n )=\\prod_t p(x_t | x_1\\dots x_{t-1}) $, where each $x_t$ represents an article after t-th editing. There are three types of operations: insert, delete, and replacement. The paper propose a model based on a modified Transformer to predict both the edit operation and the content to be generated for insertion and replacement. To predict the edit operation type (including additional no-edit action), the model uses a Transformer encoder to encoder the k previous version of history (if $k=1$, it is just based previous version) and a simple autoregressive MLP to predict the type of editing operation for each of the token. For consecutive insertion and replacement edits, the model will take the average contextual representation of the span to generate tokens (together with the span start and end index and edit type embedding).\nFor evaluation, the paper constructs two datasets with editing history: WikiRevisions (from wikipedia editing history with some cleaning) and CodeRevisions (from 700 python repositories on Github with MIT license, over 1000 commits and 500 stars).\nThe paper propose a set of three metrics: operation perplexity, generation perplexity (given operation), and the editing perplexity combining both. On these metrics, the paper shows the proposed EditPro model improves over baseline LEWIS method on WikiRevisions data. It also shows more edit history is helpful in prediction, therefore EditPro based on three previous revision history is better than that based on one immediate previous version. \n\nThe paper also propose three downstream tasks for evaluation: editing given the text comment (from the commit message), edit-conditioned generation, and edit-condition classification. \n", "summary_of_the_review": "Reason to accept: new problem setup for editing based text generation, two new datasets, and evidence of multiple history helps generation. \n\nReasons to reject: one metric is questionable, examples showing the method not working, missing human evaluation and a straightforward baseline. Without those, it is hard to judge the quality of the generation. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "bbwimoj7UY0", "reviewer": "Reviewer_eci3", "summary": "", "questions": "", "limitations": "", "rating": 3, "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": "Strengths:\n1. The idea is simple and intuitive\n2. The paper is well organized and easy to follow\n\nWeaknesses:\n1. My main concern is the motivation of this work. It is not clear why interactively editing is better than single-step editing. The authors need to elaborate on the source of the gain in more detail.\n2. Similar idea (interactively editing) is already presented in work[1]. The authors do not take this method as a baseline, or at least they should thoroughly discuss the differences with this work.\n3. In section 6, the paper only compares their model to a limited baseline, making the experiment less convincing. The paper needs to add some baseline, especially in the dataset CODEREVISIONS.\n\nReferences: [1] Shaohan, Yu, Furu, Ming (2018). Text Morphing. ", "summary_of_the_paper": "This paper proposes a new task of modeling editing processes to model the whole process of iteratively generating sequences. To tackle the new challenge, the authors propose a conceptual framework to describe the likelihood of multi-step edits and describe neural models that can learn a generative model of sequences based on these multi-step edits. The Experimental results show that modeling editing processes improve performance compared to previous single-step models of edits on related downstream tasks and the proposed task.", "summary_of_the_review": "Please refer to the Weaknesses in Main Review, I hope the authors could give a clear answer ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "EF-pXRnr5Lk", "reviewer": "Reviewer_EhJP", "summary": "", "questions": "", "limitations": "", "rating": 5, "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": "Strengths:\n - Modeling the task of sequence editing as a multistep, iterative process. Almost all previous methods model sequence editing as a single-step process.  The proposed method is technically sound.\n\n - Edit processes are modeled on a larger scale e.g. document-level edits. \n\n - Modeling multi-order edits up to the order of 3, does result in improved performance (but only in comparison to the same model with an edit order of 1)  \n\n - New proposals for datasets and metrics related to iterative sequence editing: Edit Perplexity, Generation Perplexity, and Operation Perplexity. (However, the model's design explicitly favors these metrics.)\n\nLimitations / Concerns\n \n- Comparisons with single-step edit models: There are prior works on iterative sequence editing that utilize single-step edit models and find them useful. E.g. see Table 4 in [1]. How effective is the proposed model in comparison to these methods?\n\n - The paper starts with the motivation that \"Revising and editing are a central part of the human creative workflow, with most original content being developed not in a single iteration, but in many iterations with each more refined than the last\". I agree with the authors on this part. However, the datasets considered in this paper may not be well aligned with this motivation. E.g. in many cases, Wikipedia edits and code edits may not be just stylistic edits required for improving sentence formation. A good portion of edits might also introduce new information as well. \n\n - Grammatical Error Correction is a very well-known task in the domain of sequence editing and aligned with the motivation of iteratively refining and editing a given sentence. The experimental observations could have been more convincing if the authors could show that their method is more effective than prior single-step edit methods for such tasks.\n\n - The proposed evaluation metrics in Section 5.2, are very much tied to the model design (Eqn 6). Thus the proposed metrics may naturally favor the model over other models which were not trained using similar objectives. Hence, I feel that the proposed model requires a more rigorous evaluation. \n\n - The proposed method is only compared with a single baseline. In the related work, authors do acknowledge prior work on sequence editing. However, a comparison with those methods is currently not provided.\n\n - Details about the posterior regularization being used in Section 6.2 are not fully clear.\n\n - It would be interesting to know how important is the order of edit modeling, in Table 2 and Table 3? Does performance gains diminish beyond order 3?\n\n\nReferences:\n\n[1] Parallel Iterative Edit Models for Local Sequence Transduction (https://aclanthology.org/D19-1435.pdf) \n\n", "summary_of_the_paper": "This paper considers the task of explicitly modeling the process of editing sequences in an iterative manner, while the prior approaches for sequence editing are usually single-step editing methods. Thus, the proposed method is significantly novel. The proposal is to decompose the editing process into (i) predicting edit operations (insert, substitute, delete, and keep) and (ii) generating spans corresponding to those operations. Both these steps are conditioned on the previous versions of the input sequence and its edit history. The model first predicts edit operations conditioned on previous documents and then revises the document conditioned on edits and previous documents.\n\nIn my opinion, the experimental section can be improved significantly. It currently lacks sufficient comparisons with prior work to demonstrate the efficacy of iterative edit modeling over prior methods of single-step editing. The proposed evaluation metrics also seem to be directly aligned with the design of the proposed model, hence comparisons with other models on these metrics might be unfair.", "summary_of_the_review": "This paper investigates an interesting problem of modeling the iterative process of editing the sequences. The ideas proposed seem to be technically sound and novel.\n\nHowever, I am not convinced with the tasks, metrics, and comparisons with prior work on sequence editing. \n\nMetrics: In my opinion, metrics seem to be directly aligned with the model's design, and hence comparing with models which do not optimize these metrics could be unfair.\n\nTasks: Tasks like Grammatical Error Correction or paraphrasing are widely known and directly aligned with the paper's motivation of iteratively editing. The effectiveness of the proposed method on such tasks can make experiments significantly more convincing.\n\nComparisons with prior work: The paper compares with only one baseline, while it does acknowledge well-known and recent papers in the domain of sequence editing. The paper rightly argues that most of the prior work models sequence editing as a single-step process. However, the significance of modeling edits in multiple iterative steps will be more evident after comparisons with single-step edit models.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper introduces a novel task (i.e., modelling the iterative process of editing sequences) and proposes a Transformer-based architecture to address it tractably. The paper also elects a number of metrics that are argued to shed enough light onto the merits of the proposed architecture. 

In our view, the current version is not ready for acceptance. Here are some of the reasons I'd highlight: 

* It is not entirely clear to us that the task in consideration has enough substance to grant acceptance nor that it speaks to a large enough audience. Perhaps the challenges identified here are more general and the developments for this task can be extended to related generation problems? If so, this is something one could consider for a revised version of the paper. 
* The motivation does not seem to align well with the datasets used to demonstrate the task. Perhaps the difficulty to find a dataset that matches the motivation is an indication that the task and its challenges are tad too specific. 
* It's the impression of more or less everyone involved that the paper lacks comparisons, and that the evaluation is not thorough enough, and the rebuttal did not ease our concerns sufficiently.
Decision: Reject