A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing
Authors: Oren Tsur
Yoav Tulpan
Venue: EMNLP 2023 Main (2023)
Keywords: Discourse
dialogue
style
TL;DR: see paper title :)

Abstract: Online social platforms provide a bustling arena for information-sharing and for multi-party discussions.  Various frameworks for dialogic discourse parsing were developed and used for the processing of discussions and for predicting the productivity of a dialogue. However, most of these frameworks are not suitable for the analysis of contentious discussions that are commonplace in many online platforms. A novel multi-label scheme for contentious dialog parsing was recently introduced by Zakharov et al. (2021). While the schema is well developed, the computational approach they provide is both naive and inefficient, as a different model (architecture) using a different representation of the input, is trained for each of the 31 tags in the annotation scheme. Moreover, all their models assume full knowledge of label collocations and context, which is unlikely in any realistic setting. 

In this work, we present a unified model for Non-Convergent Discourse Parsing that does not require any additional input other than the previous dialog utterances. We fine-tuned a RoBERTa backbone, combining embeddings of the utterance, the context and the labels through GRN layers and an asymmetric loss function.
Overall, our model achieves results comparable with SOTA, without using label collocation and without training a unique architecture/model for each label. Our proposed architecture makes the labeling feasible at large scale, promoting the development of tools that deepen our understanding of discourse dynamics.

A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing
Yoav Tulpan
Oren Tsur
Ben Gurion University of the Negev yoavtuêpost bgu. ac. il
Ben Gurion University of the Negev
orentssurbgu.aac.
Abstract
mar et al., 2017: Zhang et al., 2018), and increase political and societal polarization (Kubin and von Sikorski, 2021; Lorenz-Spreen et al., 2022).
Online social platforms provide a bustling arena for information-sharing and for multi- party discussions. Various frameworks for di- alogic discourse parsing were developed and used the processing of discussions and predicting the productivity of dialogue. How- ever, most of these frameworks are not suitable for the analysis of contentious discussions that are commonplace in many online platforms. A novel multi-label scheme for contentious di- alog parsing was recently introduced by Za- kharov et al. (2021). While the schema is well developed, the computational approach they provide is both naive and inefficient, ferent model (architecture) using a different representation of the input, is trained for each of the 31 tags in the annotation scheme. More- over, all their models assume full knowledge of label collocations and context, which is un- likely in any realistic setting. In this work, we present a unified model for Non-Convergent Discourse Parsing that does not require any ad- ditional input other than the previous dialog utterances. fine-tuned a RoBERTa back- bone, combining embeddings of the utterance, the context and the labels through GRN layers and an asymmetric
The ability to parse contentious discussions at a large scale bears practical and theoretical benefits. From theoretical perspective would allow the research community at large (social scientists and computational scientists alike) to better track and understand conversational and socictal dynamics. From a practical perspective, was found that early intervention by a human moderator or facilitator can improve the productivity and focus of dis- cussion (Wise and Chiu, 2011; Chen et al., 201 8). Discourse parsing can be the first step in develop- ing assistive moderation tools that can be employed at scale and promote more productive discourse.
dif-
It is commonly argued that the convergence of views indicates the success (or productiveness) of conversation (Barron, 2003; Dillenbourg and Fis- cher, 2007; Teasley al., 2008; Lu al., 201 I). This perspective has been reflected in discourse an- notation schemes that were proposed through the years (Teasley et al., 2008: Schwarz et al., 2018). However, the equation of productiveness with con- vergence is being challenged based on both theo- retical and empirical grounds, non-convergent discussions can be very productive, as they serve as fruitful venue for the development of dialogic agency (Parker, 2006; Lu al., 2011; 1; Trausan- Matu al., 2014; Kolikant and Pollack, 2015; Hen- nessy et al., 2016; Kolikant and Pollack, 2017).
function. Overall, our model achieves results comparable with SOTA, without using label collocation and without training unique architecture/model for each label. Our proposed architecture makes the la- beling feasible at large scale, promoting the development of tools that deepen under- standing of discourse dynamics.
1 Introduction
The non-convergence perspective inspired a novel annotation scheme that was recently introduced by Zakharov al. (2021). Its organizing principle is responsiveness, rather than acceptance and conver- gence of ideas productive discussion is one in which the interlocutors use speech acts that exhibit high responsiveness, while acts of low responsive- ness deem the discussion unproductive. It is impor-
Online discourse has become major part of mod- communication due to the proliferation of on- line social platforms that allow people to easily share their ideas with a global audience. However, the ease of communication has also led to more heated debates and arguments that sometimes de- volve into personal attacks (Arazy et al., 2013; Ku-

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Well stay tuned for my "CMV: Down with bourgeoisie scum" thread.
Do you think it's reasonable treat flat earth and round earth equally too? Assuming good faith of course.
Sarcasm
Clarification Request
There is no bourgeoisie in the traditional sense. Even Trump lower class for while.
Counter Argument
Of course.
Answer
Counter
Do you think it's even possible tho be flat earther in good faith? Seeing as how there is mountains upon mountains of evidence against it?
Aggressive
Argument
Well you're wrong both accounts.
No Reason Disagreement
Critical Question
Nope. He poorer than you've ever been. He was never working class, he was lower class.
Direct
Counter Argument
think it's very hard given
how evident it is, but
Answer
happened, then yes, conversation should arise.
Direct
No he wasn't.
(a) Low responsiveness snippet
(b) High responsiveness snippet
Figure I: Two annotated snippets extracted from the CMV dataset, displaying low responsivenes: (claim: no need for privacy regulation), and nigghresponsivenes discourse (claim: online cancel culture is ineffective). Labels are indicated in the green rectangles the left/right of each utterance.
tant to note that responsiveness is not the mere act of producing a response, but the act of responding in good faith. The application of this schema is illustrated by the two snippets in Figure 1. In the short exchange in Figure la, the first speaker uses sarcasm and later responds aggress sesssiveelyy ively Counter Argument. The dialogue then goes from bad to worse with series of Direct No utterances. The other discussion (Figure Ib) demonstrates how Counter Argument and Critical Question push for a reasoned an- swer, even though the topic is highly divisive. An- other interesting observation that applies to many online discussions is the way argumentation tends introduce sub-topics rhetoric ?
definition of the computational task is presented in Section 4.1.
The need for dedicated discourse schema and the development of the tagset were well motivated by Zakharov et al. (2021). The authors released an annotated dataset of 10K utterances and demon- strated the feasibility of learning the annotation task. However, their computational approach suf- fers from number of drawbacks: First, they cast the prediction task as a binary classification and trained a model for each tag separately. Second, considering the prediction of tag to an utterance thi they assumed access to oracle providing complete and accurate knowledge of gold labels of preceding utterances and the correct binary assign- ment of all other tags for u4. This very strong as- sumption not realistic in any real-world scenario. Finally, the results they report were achieved after feature engineering and an extensive grid search on the classifier and the features space. Consequently, each tag is predicted using different classification framework, based uniquely crafted feature set.
Subscribing this annotation scheme, the Con- versational Discourse Parsing (CDP) task can be viewed -fqutence-f-uuterrances to sequence-of- sets task: an utterance can be labeled by multiple labels concurrently. For clarity, we provide a brief explanation of the tagset in Section 3. A formal
'The sarcastic remark (Figure la) follows an argumen- tation line asserting that privacy form of capital (in the Marxist sense) and
In this work, present N-CoDiP unified au- toregressive transformer for Non-Convergen Dis- course Parsing. The model is trained predict all labels together without using any external knowl- edge provided by an oracle. N-CoDiP performance (F-score macro and weighted averages) is compara-
maintains social power imbalance. "The discussion in Figure la originated from an opening statement about privacy, discrimination and (social) power imbalance. The discussion in Figure 1b stemmed from a discussion about "cancel culture' and the reference to 'flat carthers' is rhetorie device used to establish a common ground.

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ble with the best results reported by Zakharov et al. (2021) without suffering from any its drawbacks. the study of the effects of forum norms and mod- cration, as well as persuasive text analysis and lin- guistic style accommodation, e.g., Tan et al. (2016); Khazaei et al. (2017); Musi et al. (2018); Jo et al. (2018); Xiao and Khazaei (2019); Ben-Haim and Tsur (2021); Chandrasekharan et al. (2022).
CMV and Discourse Owing to the high quality of its discussions, CMV discussions are commonly used as a data source for various NLP and social science research, ranging from argument mining to
Our proposed model uses the RoBERTa architec- ture (Liu et al., 2019) the backbone. We use SimCSE (Gao et al., 2021) for sentence embed- ding and feed preceding utterances through Gated Residual Network (GRN) (Lim et al., 2021). The model is fine-tuned using an asymmetric loss func- tion that was recently demonstrated to improve performance in imbalanced multi lubll assignment in vision (Ridnik et al., 2021). To the best of our knowledge, this is the first application of this loss function in this domain. We provide detailed de- scription of the architecture in Section 4. Results and analysis are provided in Section 6.
A Argumentation and argument mining Argu- ment mining is another related line of research, for a comprehensive survey see (Lawrence and Reed, 2020). Argument mining is done on long-form documents, e.g., Wikipedia pages and scientific papers (Hua and Wang, 2018) or in dialogical con- texts, e-g. Twitter, Wikipedia discussion pages, and Reddit-CMV (Tan al., 2016; Musi al., 2018; Al Khatib et al., 2018). Argument mining enables a nuanced classification of utterances into discourse acts: socializing, providing evidence, enhancing understanding, act recommendation. question, con- clusion, and so forth (Al Khatib et al., 2018). Most of the argument mining work is aimed at identify- ing stance and opinionated utterance or generating arguments supportive evidence end users ducting formal debates (Slonim et al., 2021). Our work is inspired by these works, although our focus is on the way discursive acts reflect and promote responsiveness, rather than simply labeling texts as bearing 'evidence' or posing a 'question'. More- over, while our focus is contentious convereet discussions, we wish to characterize discussions win-win win, rather than a competition.
2 Related Work
Conversational Discourse Parsing There have been numerous dialog corpora collected and la- beled with various schemes to model discourse structure. (Jurafsky al., 1997) presented the Wwitchboard-DAMSS dialog act schema on dataset of cooperative, task-oriented dialogues be- tween pairs of interlocutors in phone conversations (Godfrey et al., 1992). This was extended in (Cal- houn et al., 2010) to allow for more thorough anal- ysis of linguistic features in dialog. There have been multiple studies approaching the dialog act classification problem with deep neural networks including transformers, with some emphasizing the importance of integrating context information from previous utterances (Liu et al., 2017; Saha et al., 2019; Santra et al., 2021; Želasko et al., 2021). The Wwitchboard-DA.M. corpus is two party dis- course analysis schema, which is different from the multi-party discourse parsing schema presented in (Zakharov et al., 2021) and modeled in this work. Multi-party dialog corpora such as STAC (Asher al., 2016) as well as the Ubuntu unlabeled corpus (Lowe et al., 2015) and its labeled extension the Molweni discourse relation dataset (Li et al., 2020) are more closely related to the current task, though the discourse is not contentious and the utterances tend to be quite short when compared to messages in the CMV forum debates. Another key difference between these and the CDP corpus is that in the latter, the label scheme oriented towards a more basic understanding of the components of produc- tive discourse, while the former is more focused on characterizing basic dialog acts.
Multi-label classification Regarding imbalanced multi- classification, the existing approaches include over- and under-sampling the relevant classes, as well as adapting the classification ar- chitecture using auxiliary tasks to prevent over- fitting to the majority classes (Yang et al., 2020; Tarekegn et al., 2021). Another approach is to ap- ply imbalanced loss functions to neural network models such as weighted cross entropy and focal loss, which is closely related the Asymmetric loss function incorporated this work apart from some key improvements detailed in section 4.2.5 (Lin et al., 2017; Ridnik et al., 2021).

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3 Data
4 Computational Approach
4.1 Task Definition
Change My View (CMV) data CMV is self- described as "A place to post opinion you accept may be flawed, in an effort to understand other perspectives on the issue. Enter with a mindset for conversation, not debate."3 Each discussion thread in CMV evolves around the topic presented in the submission by the Original Poster (OP). Each discussion takes the form of a conversation tree in which nodes utterances. A directed edge o <- u denotes that utterance w is direct reply utterance t. A full branch from the root leaf node is sequence of utterances which reflects a (possibly mutti-particciaat)) discussion. CMV is heavily moderated to maintain a high level of discussion. CMV data has been used in previous research on persuasion and argumentation, see brief survey in Section 2.
We define the discourse parsing classification prob- lem as follows: Given a tagset T and a sequence of utterances ing sequence of labels = 11,,.. bn such that it maximizes the probability P(T[U). It is important to note that each is actually a set of labels from the T such that C T, making this sequence sequence-of- sets task. The sequence of utterances processed sequentially in an autoregressive man- ner. That is, when tagging uy the model already processed 2j through u;-1 and Ujsi are masked.
U1 ..., Un: find correspond-
4,2 N-CoDiP Architecture and Components
Given a sequence of utterances U1 ... Uhy utter- ance uy is processed along with its context o; - the utterances preceding it (u,,..., 14-1). First, we use the pretrained model to get two embedding vectors t7 and di representing and c, respectively. We then use two GRN blocks: The first combines & with (--1, the label embeddings vector produced in the previous iteration (processing 4i-1). The second GRN block combines the resulting vector with ui for combined representation. This repre- sentation is passed to block of MLP classifiers which produce 4, a vector assigning the likelihood of each tag t E T for uG An illustrative figure of the model is provided in Figure 2. In the remainder of the section present the components of the N-CoDiP architecture in detail.
Annotation scheme tagset The Contentious Dis- course Parsing tag schema developed by Zakharov et al. (2021) consists of 31 labels that fall under four main categories: discursive acts that promote further discussion; discursive acts exhibiting or ex- pected to cause low responsiveness, tone and style; explicit disagreement strategies. For convenience, the full schema and the labels' definitions are pro- vided Appendix B.
The annotation scheme allows collocation of la- bels assigned to the same utterance some labels reflect style while others reflect the argumentative move. For example, the utterance "well you's 're wrong on both accounts." (Figure la) carries an Aggressive tone, providing No Reason for the disagreement it conveys.
4.2.1 Text Representation
The representation of the target utterance u, and the context utterances G are produced separately in a slightly different ways. ti, the representation of uy simply the [CT.S] token vector obtained by passing u; to the pretrained model. The context representation oj is the [CLS] of the concatenated word-tokens of the context utterances, using the [SEP] token to separate between utterances in or- der to allow context utterances to attend to each other. That is, the context utterances are passed sequence (i-=sSSSjuu---SSEPPP) where the length of the context and u, the sequence of tokens in the jth utterance.
The annotated dataset The dataset released by (Zakharov al., 2021) is composed of 101 discus- sion threads from CMV. These threads (discussion trees) have a total of 1,946 branches composed of 10,599 utterances (nodes) made by 1,610 unique users. The number of labels assigned to the nodes in the dataset is 17.964.
4.2.2
Context's Label Embedding
Phttps://www. wwtp://wwwwww...cooom com/e/
We define label embedding function Fmb(() E dd where is the transformer embedding dimen- sion (in our case, 768). In cases where a previous
changemyyiew//iike index (accessed 1/17/23)
"The dataset was made available here: http:/ /bl t..
1y/3XIMeD1 (last accessed: 1/17/23).

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We use GRNs to combine the textual embedding of the context (c)) with pooled label embeddings (l), and again to combine the result with di the embedding vector of the target utterance.
Coniiieed
Asymmetiic loss
Similrity loes
4.2.4 Multi-head MLP
In the final layer, the combined representation is passed to d independent MLP heads, with d being the number of labels in the tagset. Given the last hidden layer output 2, the model's prediction for the i'th label is:
MLP dissiiter:
Author 1-hot 1 poo D
Combited Ieprrsentar on
1 = (Wi + bi,1) b.,2)
GRN
4.2.5 Asymmetric Loss
GAN--co
The Asymmetric Loss recently developed handle unbalanced multi-label classification tasks in the field of computer vision (Ridnik al., 2021). The asymmetric loss applies scaling decay factor to the loss in order to focus on harder examples. However, different decay factors are used for in- stances with positive and negative gold labels: a larger decay factor (y- > 7+) to the negative ex- amples. Also, it employs hard lower cutoff for model confidence scores to discard too-easy examples.
Cuntact utterance Iecreoentation
Tergei utsorances represettation
Ausiitrry NMP
embedding pooling
Transfo cockboone
chamed contect utterancess
Target uttararce
Figure 2: N-CoDiP architecture, Dotted arrows indicate optional components.
utterance is unlabeled, we add an additional embed- ding that represents untagged context utterance. We combine the label embeddings of the multiple utterances in the context using mean-pooling.
Asymmetric loss was used for relation extraction between entities in a given document by (Li et al., 2021), but still underexplored in the NLP context and was never used for conversational discourse parsing.
4.2.3 Context Integration with GRNs
Gated Residual Networks (GRN) (Lim al., 2021) were recently proposed in order to combine pri- mary input vector with context vectors of multiple types and unknown relevance. GRNs were demon- strated to be especially beneficial when the dataset is relatively small and noisy.
It allows the model to learn the task despite positive to negative label imbalances, which are often hindrance neural network performance. The AL (Asymmetric Loss) function is defined over the positive cases L4 and the negative cases L.:
AL( = (1- tog
4 EE L+ log(1 Im) 4 L-
Formally, given vector x and context vector
GRN(x,c)
In = max((, - m, 0), and m is the lower hard cut- off of model confidence scores for negative labels.
LayerNorm(( + GaledLinear((n)) 1h Win2+bb
4.2.6 Auxiliary Next Message Prediction Task Incorporating an auxiliary prediction task to the training pipeline often improves results, especially over relatively small datasets for which pretrained models tend to overfit (Chronopoulou al., 2019; Schick and Schütze, 2021). Drawing inspiration from (Henderson et al., 2020), we incorporate Next Message Prediction (NMP) as an auxiliary task.
122 ELU (W2 + Wge +2) Gated[inear(1)) oW47 + b4) @ (W5y + bs)
Where W;(+) + by is linear transformation main- taining the input dimension d, and (-) (-) is an Exponential Linear Unit (Clevert et al., 2015).

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the NMP the model maximizes the cosine similarity of two consecutive messages in the conversation tree, and minimizes that of non-consecutive ones. That is, the training objective of this auxiliary task is to minimize LPM defined as:
Exhaustive Grid (X-Grid) The best results reported by Zakharov et al. (2021) achieved using a different model for each label, exten- sive feature engineering, external resources (LIWC, DPTB discourse labels), an Oracle providing preceding and collocated labels and exhaustive grid-search in binary classifica- tion setting (per label).
W
k Σ
LNMP- ΣΣ
Suu, Uj)
S(u, uz)
2. Zakharov Transformer (Z-TF) The same Transformer architecture used by (Zakharov et al., 2021) applied in a "clean" setting, that is, without the use of an oracle or special (ex- ternal features. The use of this baseline al- lows proper evaluation of our model against prior work.
Where S is similarity function (we cosine similarity), & is the batch size for the main Dis- course Parsing (DP) task, and k the number of negative samples, which are simply the other ut- terances the batch. We also attempted to add more challenging negative samples, i.e., samples that are sampled from the same conversation tree and are therefore assumed to belong to the same semantic domain. The final loss function to be minimized in training is:
Pretrained Models We consider two pretrained models for text representation: the vanilla RoBERTa (Liu et al., 2019) and the RoBERTa- SimCSE that was optimized for sentence embed- ding (Gao et al., 2021). We indicate the pretrained model that is used in subscript: CoDiPy for the Vanilla RoBETRa and CoDi for the SimCSE version.
L aLDp + LNNMP
LDP is the Asymmetric loss described in section 4.2.5, and a E [0.95, 0.99] is a weighting factor for the different objectives.
Evaluation Metries Keeping in line with previ- work F-score (F1) for individual labels. We report both macro and weighted F-scores sults aggregated by label category. Macro F-score being the mean score, and weighted being the mean weighted according to the support of each class:
4.2.7 Speakers' Turn Taking
We expect that the conversational dynamics in a dialogue of only two speakers are may be different than those in multi-speaker dialogue. Moreover, even in a multi-speaker dialogue, the discourse be- tween speakers and B may be different that the discourse between A and C. We therefore add le 1 one-hot vectors representing the the speakers of the target utterance u; and the k preceding utterances used for context. That is, given k = and the se- quence of utterances "f= uz (postscript denotes the speaker), get the following vectors:
Fa (Fi,.., Fb)) E F.
k
FWeighted (Fi, FR)
F wa
where k is the number of labels a particular label category (e.g., Promot in Discourse, Di sagreement Strategies). w; is the prior probability of a specific label 4 being true in the dataset, which is comprised of samples:
[1, 0, 0, 0), [0, 1, 1),
0, (0,0,,
(1,0, 0,
indicating that u; and u;-3 were produced by the same speaker (A), while u:-2 and u4-1 where pro- duced by two other speakers (B and C). These vec- tors were concatenated and appended to the final combined representation vector.
Σ 1
Wi
n
The prior probabilities are presented in table in Appendix A.
5 Experimental Settings
Baselines We compare our N-CoDiP architecture to previously reported results in (Zakharov et al., 2021). We focus on two sets of reported results:
Execution Settings We trained the N-CoDiP model for 4 epochs optimized using the AdanW W optimizer (Loshchilov and Hutter, 2018) with a

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Category
N-CoDiP CSE
N-CoDiP RCB
N-CoDiP
Z-TT
X-Grid
All
0.3971
0.573
0.371
0.563
0.378
0.565
0.113
0.338
0.382
0.6067
Promoting Discussion
0.461
0.709
0.426
0.692
0.439
0.690
0.158
0.546
0.5601
0.833
Low Responsiveness
0.312
0.337'
0.276
0.304
0.284
0.058
0.057
0.335
Tone and Style
0.346
0.370
0.320
0.352
0.334
0.361
0.054
0.064
0.304
0.326
Disagreement Strategies
0.422
0.5071
0.408
0.497
0.407
0.499
0.142
0.170
0.370
0.451
Table I: Average F-scores per label category for each model. Values are arranged as (Macro, Weighted) pairs. N-CoDiP architectures differ in the loss function used: Asymmetric Loss (AL) or Binary Cross Entropy (BCE), and the pretrained model used: Contrastive Sentence Embedding (CSE) or the vamilla RoBERTa (V); 7-TF is the BERT architecture used by Zakharov et al. (2021); X-Grid are best results reported in prior work using an oracle and applying exhaustive grid search over parameters and models for each of the labels. A f indicates best results overall. Best results achieved by a transformer architecture without an oracle or feature engineering are in bold face.
batch size of 32. We used linear warm up and de- cay the learning rate, with the warm up period consisting of first 30% of the training iterations reaching maximal 7 = 10-5 learning rate and de- caying back to zero over the remaining 70% of training iterations. We restrict our experimentation to contexts of up to k utterances and set k = 4. For the Asymmetric loss we used the default parame- ters 71 1:7 4; m 0.05.
and weighted scores. Moreover, N-CoDiP CSE outperforms X-Grid over three out of the four label categories (Low Responsiveness, Tone Style, Disagreement Strategies), and obtains a higher Macro average F-score aggregated over all labels.
Evaluating the impact of the loss function (Asym- metric vs. Binary Cross-entropy) we find that the asymmetric loss is consistently better. We also find that the most significant improvements are achieved over the Low Responsiveness, Tone Style categories, for which the priors are relatively low (see Table in Appendix A), This is also evident by comparing the gains in the macro averages vs the gains in the weighted aver- age: 0.026 and 0.01, respectively.
Computational Cost We trained our final im- plementation of the model 20 times (4 model well
variations 5 fold cross validation), additional implementations during its develop- ment, each taking between 2 and 3 hours on Nvidia Gelorce 12GB GPU. The model contains 130,601,503 parameters.
Also, for most labels the use of the pretrained RoBERTa-SimCSE) achieves better results than the vanilla RoBERTa, gaining 0.019 macro-F points, and only 0.012 points the weighted score.
6 Results and Analysis
6.1 Results
All reported results are the average of 5-fold cross validation. Partition of the data in each fold was done based on discussion trees rather than conver- sation branches in order to avoid leakage from the train set to the test set.
Category
N-CoDiP :=1
N-CoDiP A=
All
0.397
0.573
0.389
0.573
Promoting Disc.
0.461
0.709
0.426
0.699
Low Resp.
0.312
0.337
0.298
0.328
Tone and Style
0.346
0.370
0.338
0.328
Disagreement Str.
0.422
0.507
0.422
0.506
Macro and weighted F-Score over the whole tagset and by label's categories are provided in Table 1. Prior probabilities and detailed results for each la- bel are omitted for clarity and due to space con- straints but are available at Appendix A.
Table 2: Average F-scores per label category for the N-CoDiP model given context length and 4 context length. Values are (Macro, Weighted) pairs.
6.2 Discussion
The results that were reported by prior work (X- Grid) are presented as a guide, but are shaded since the X-Grid setting does not allow fair comparison. We expand on this in the discussion.
N-CoDiP vs. X-grid While N-CoDiP achieves best results in most cases, the X-Grid achieves higher weighted score the aggregation of all la- bels, and significantly outperforms CoDiP in the Promoting Discussin i category. It im- portant to reiterate that the X-Grid setting does not allow a fair comparison. Not only were each
N-CoDiPAL CSF consistently outperforms other unified models trained to predict all labels without any prior or externl knowledge in both Macro

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the X-Grid results obtained by different classifier based on different feature set, it combines heavy feature engineering of external reasources such as LIWC categories (Tausczik and Pennebaker, 2010), DPTB labels (Prasad et al., 2008; Nie et al., 2019),
not yield any improvement. We believe that the ways contexts (and different contextual signals) are integrated and attended to should be further investi- gated in order to leverage the full potential of the information encoded in the context.
an Oracle providing preceding and collocated la-
The unimpressive contribution of auxiliary task Incorporating an auxiliary prediction task to the training pipeline is reported to often improve re- sults, especially when fine-tuning over relatively small datasets (Chronopoulou et al., 2019; Hender- son al., 2020; Schick and Schütze, 2021). We experimented with number of settings for utter- ance proximity prediction to no avail results were not improved in any significant way. We plan to explore this further in the future.
bels (classification is binary per label), and an ex-
haustive grid search over the model family, fea- tures, and hyper parameters. In contrast, the rest of the results in Table 1 are achieved using a single unified model without incorporating any auxiliary resources except RoBERTa, and no Oracle hints.
N-CoDiP vs. Z-TF Although the results pre- sented above establish the effectiveness of a single unified model, we observe a stark difference in performance between all variants of the N-CoDiP architecture and the Z-TF. This difference begs the question what in the architecture makes such an impact, given both approaches rely on the same pre- trained BERT based architecture. We hypothesize that the combination of the multi-head classifier and the Asymmetric loss objective (Sections 4.2.4 and 4.2.5) drive CoDiP performance up. The indi- vidual classifiers add another layer which enables the model to learn a unique final hidden representa- tion for each label. We have found this to be quite effective in mitigating the label bias. Indeed, we ob- serve that even though Z-TF is inferior to CoDiP, it does perform reasonably well on the most frequent label (CounterArgument) p 0.635, see Ta- ble 3 in Appendix A). In addition, the asymmet- ric loss function provides significant gains for less common labels, promoting the hypothesis that the poor Z-TF performance stems from a label imbal- ance, common issue in multi-class neural network based classifiers (Xiao et al., 2019).
Conclusion and Future Work
Theoretical framework and empirical evidence mo- tivates the need for discourse annotation schema that reflects discursive moves in contentious dis- cussions. We introduced N- N-CoDiP, a unified Non- Convergent Parser that outperforms pre- vious work in a discourse parsing task based on the scheme that was recently developed and shared by Zakharov et al. (2021).
We have demonstrated that using GRN layers, pre- viously used for multi-horizon time-series forccast- ing by Lim et al. (2021) and an asymmetric loss function, previously used in computer vision by Ridnik et al. (2021) is especially beneficial to the task at hand, given the relatively small dataset, the imbalanced tagset, and the multi-label setting.
Future work will take theoretical and computa- tional trajectories. A robust error analysis will be done with respect to the theoretical framework be- hind the annotation scheme. Computationallyy we will investigate better ways to better leverage the abundance of structured unlabeled data (thousands of discussion on CMV and other platforms) as an auxiliary task, and achieve a better integration the context turn turn-taking structure with the model.
Finally, unlike the autoregressive architecture the CoDiP models, Z-TF naively uses the Transfomer as a non-autoregressive classifier. Consequently, while it processes preceding utterances to provide context to the target utterance, it does not leverage the labels that were predicted for the context.
Context length and modaitty Surpris- ingly, we found that adding as many context utter- ances the encoder can take resulted in degraded performance, comparing to using only the single immediate context (k: 1). A comparison between context length of and 4 is provided in Table 2. Similarly, we find it surprising that adding the au- thor turn taking information (see Section 4.2.7) did
Limitations
The main limitation of the paper is the size of the dataset, given the large and imbalanced tagset and the complex and nuanced discourse annotation scheme. We believe that expanding the dataset and maybe reconsidering some nuances in the annota- tion scheme would mitigate the issue.

---

9 Ethics and Broader Impact
discussions. In Proceedings of the 16th EACL Confer- ence: Main Volume, pages 1876-1886.
This paper is submitted in the wake of tragic ter- rorist attack perpetrated by Hamas, which has left our nation profoundly devastated. On October 2023, thousands of Palestinian terrorists infiltrated the Israeli border, launching a brutal assault on 22 Israeli villages. They methodically moved from home to home murdering more than a thousand innocent lives, spanning from infants to the eiderly. In addition to this horrifying loss of life, hundreds of civilians were abducted and taken to Gaza. The families of these abductees have been left in ago- nizing uncertainty, as no information, not even the status of their loved ones, has been disclosed by Hamas.
Sasha Calhoun, Jean Carletta, Jason M Brenier, Neil Mayo, Dan Jurafsky, Mark Steedman, and David Beaver. 2010. The nxt-format switchboard corpus: a rich re- source for investigating the syntax, semantics, pragmat- ics and prosody of dialogue. Language resources and evaluation, 44(4):387-419.
Eshwar Chandrasckharan, Shagun Jhaver, Amy Bruck- man, and Eric Gilbert. 2022. Quarantined! examining the effects of a community-wide moderation interven- tion reddit. ACM Transactions on Computer-Humne Interaction (TOCHI), 29(4):1-26.
Bodong Chen, Yu-Hui Chang, Fan Ouyang, and Wany- ing Zhou. 2018. Fostering student engagement in online discussion through social learning analytics. The Inter- net and Higher Education, 37:21-30.
Alexandra Chronopoulou, Christos Baziotis, and Alexandros Potamianos. 2019. An embarrassingly sim- ple approach for transfer learning from pretrained lan- guage models. In Proceedings of NAACI-HLT, pages 2089-2095.
The heinous acts committed during this attack, which include acts such as shootings, sexual as- saults, burnings, and beheadings, are beyond any justification.
Djork-Amé Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2015. Fast and accurate deep network learn- ing by exponential linear units (elus). In 4th Interna- tional Conference on Learning Representations, ICLR 2016.
We fervently call for the immediate release of all those who have been taken hostage and urge the academic community to unite in condemnation of these unspeakable atrocities committed in the name of the Palestinian people. We call all to join us in ad advocating for the prompt and safe return of the abductees, as we stand together in the pursuit of justice and peace.
Pierre Dillenhourg and Frank Fischer. 2007. Computer- supported collaborative learning: The basics. Zeitschrift für Berufs-und Wirtschafsspidaaggii,, 21:111-130.
Tianyu Gao, Xingeheng Yao, and Danqi Chen, 2021. Simese: Simple contrastive learning of sentence em- beddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages
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---

A
F-scores by Label
Label/Calegory
N-CoDiP
N-CoDiPBCE
N-CoDiPBASE
Z-IF
X-Grid
Priors
Promotes discussion
TrableTransfommatin
0.118
0.09
0.092
0
0.158
0.01
Answer
0.413
0.366
0.397
0.522
0,522
0.014
Extension
0.286
0.258
0.263
0.507
0.549
0.022
Attack Validity
0,506
0.435
0.48
0.143
0.51
0.028
Moderation
0.353
0,277
0.326
0.027
0.42!
0.036
ReguestClarfficttion
0.488
0.482
0.471
0.160
0.731
0.038
Personal
0.646
0.644
0.654
0.066
0.396
0.046
Clarification
0.524
0,466
0,459
0
0,817
0.109
CounterArgument
0.818
0.813
0.805
0.775
0.9391
0.635
responsiveness
MaRcaasonDiaarremmoon
0.349
0,284
0,266
0
0.41
0.01
Agroe TorsaaoDeeee
0.391
0,261
0.3
0
0.2
0.014
Repetition
0.118
0.118
0.136
0.161
0.016
BAD
0.217
0,256
0.257
0
0.114
0.018
Neg Transformmano
0.169
0.131
0.151
0.406
0.024
Convergence
0.630
0.606
0,593
0.108
0.565
0.028
3. Tone and Style
WQualifiers
0.351
0.274
0.343
0.029
0.118
0.024
Ridicule
0.236'
0.193
0.207
0.029
0.11
0.029
Sarcasm
0.212
0,216
0.209
0.164
0.048
Aggressive
0.271
0,251
0,265
0
0.17
0.051
Positive
0.532
0.541
0.515
0.19
0.336
0.058
Complaint
0.475
0.449
0.467
0.077
0.343
0.064
4. Disagreement Strategies
Alternative
0.192
0.178
0.184
0
0.133
0.018
RephraseAttack
0.179
0.132
0.183'
0
0.077
0.022
Double Voicing
0.162
0.146
0.179
0
0.179
0.026
Softening
0.293
0,265
0,288
0,014
0,379
0.029
Sources
0.779
0.774
0.746
0.730
0.884
0.045
AgteeBut
0.473
0.481
0.459
0.106
0.058
Irrelevance
0.286
0,262
0.22
0
0.172
0.059
Nitpicking
0.760
0.763
0.786
0.447
0.791
0.061
DirectNo
0.4581
0.443
0.412
0.259
0.08
CriticalQuestion
0.636
0.635
0.618
0,224
0.722
0.128
Table Mean 5-fold cross validation F-scores the individual labels in the tag-set. N-CoDiP architectures differ in the loss function used: Asymmetric Loss (AL) or Binary Cross Entropy (BCE), and the pretrained model used: Contrastive Sentence Embedding (CSE) or the vanilla RoBERTa (V ): 7-TF the BERT architecture used by Zakharov (2021); X-Grid are the best results reported in prior work using an oracle and applying an exhaustive grid search over parameters and models for each of the labels. A & indicates best results overall. Best results achieved by a transformer architecture without an oracle or feature enginecring are bold face. Prior probabilities included.

---

B
Complete Tagset and Label definitions
Description
Tag
1. Discursive moves that potentially promote the discussion
Moderating/regultingg e.g. "let's ss get back to the topic"
Moderation
Request for clarification
RequestClar..lcctiion
Allack the validity the argument ("Who says?")
ActackValidity
Clarification previous statement (utterance)
Claritication
Informative answer of question asked (rather than clarifying)
Answer
A disagreement which is reasoned, a refutation.
Can be accompanied by disagreement strategies
CounterArgument
Building/extending previous argument. The speaker takes
Extension
the idea of the previous speaker and extends it.
A viable transformation of the discussion topic Personal statement "this happened to me")
SlableTransformat in Personal
2. Moves with low responsiveness
Severe low responsiveness: continuous squabbling Repeating previous argument without any real variation Response ancillary topic/ derailing the discussion
BAD
Repetition
Negllransfrmatiinn
Negation/disagreemmntt without reasoning
NokeassnDDsaagremmett
Convergence towards previous speaker
Convergence Agreement
The issue is deemed unsolvable by the speaker
Agreellbbsaggree
3. Tone and style
Negative tone and style
Aggressive and Blatant "this is stupid"
Aggressive
Ridiculing the partner (or her argument)
Ridicule
Complamning about a negative approach "you were rude to me"
Complaimg
Sarcasm/ cynicism /patronizing
Sarcasm
3.2 Positive tone and style
Attempts to reduce tension: respectiul, flattering, etc.
Positive
Weakening qualifiers e.g. "I'm not an expert in this topic..."
RQual 1 11ll111e ifiers
4. Disagreement strategics
Fasing tension
Softening the blow a disagreement.
Softening
Partial disagreement "I disagree only with one part of your text"
AgreeBut
Explicill taking into account other participants' voices
Doublevoicing
Using external souree support a claim
Sources
4.2 Intensifying tension
Reframing paraphrasing the previous comment
RephraseAttack
Critical question, phrasing the (counter) argument question
Critica Questioo
Offering alternative without direct refutation
Alternative
Direct disagreement ("I disagree", "thi is simply not true")
DirectNo
Refutation focuses on the relevance of previous claim
Trrolevance
Breaking previous argument to pieces without real coherence
Nitpicking
Table 4: Copied from Zakharov et al. (2021).


Reviews:
[{"review_id": "b3z56yfke1", "reviewer": "Reviewer_Ks1Q", "summary": "", "questions": "* Is the cross entropy in table 1 weighted?\n* I think i missed but i couldn't find any ablation results for the auxiliary task. In page 8 first half last para you talk about unimpressive contribution - then why not just remove it and keep things simple?", "limitations": "", "rating": 3, "confidence": 2, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "*General*: We thank the reviewers for their time and comments and we are happy that all reviewers agree about the soundness, reproducibility and other merits of the work. \n\nDetailed response:\n\n*Approach, Encoder-Decoders*: This work serves as an initial step demonstrating the applicability of a single architecture, comparing to the cumbersomeness of the approach used in  previous work. The main motivation is not only to achieve better results but to allow a comparison. Indeed, future work should utilize other seq2seq models.\n\n*Prior work about persuasion*: As the reviewers noted - the tagset is very different. Moreover, our motivation is somewhat broader than just labeling dialogue acts for persuasiveness - on the contrary, we aim to understand the benefits of  “failed” persuasion (=non-convergent discussions). \n\n*Passing previous utterances directly+ablation*: We have these results but had to exclude them due to space constraints. We will be reporting on these given the extra page granted in the camera ready. \n\n*CE*: We reported with unweighted BCE. We also experimented with a weighted version but didn’t observe any significant difference. \n\n*Auxiliary task*: It is a common and straightforward approach that was expected to improve results. We report on its shortcomings because we believe it is a surprising negative result. We also wanted to vent our frustration :)", "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": "* The paper is generally well written and easy to follow. The problem is motivated well.\n* The approach is well justified and results look sound and reproducible. ", "reasons_to_reject": "* The approach seems primitive in that they only use a single roberta model. I'm wondering how encoder decoder models (T5, Bart, etc) pretrained on dialogue data perform in this task.\n* There are works doing discourse parsing (for persuasion and negotiation) which aim to label each utterance in a dialogue. This work fails to compare with the methods employed in those even though the label set (or the specific problem) is different. (https://arxiv.org/abs/1906.06725, https://arxiv.org/abs/2106.00920, etc -- I'm not aware of the latest literature to be honest but these are some old papers I could find).\n* What if you directly pass previous utterances to the encoder? Are there any ablations to show the need of the GRN?\n* Results lack statistical significance tests.", "questions_to_authors": "* Is the cross entropy in table 1 weighted?\n* I think i missed but i couldn't find any ablation results for the auxiliary task. In page 8 first half last para you talk about unimpressive contribution - then why not just remove it and keep things simple?", "paper_topic_and_main_contributions": "The paper introduces an architectural methodology for contentious dialogue parsing. The proposed method is based on roberta, passing the dialogue context and previous labels through GRN layers and use an asymmetric loss to train their model. They compare their approach with one baseline (which uses naive methods, side information and oracle labels) and show improved / similar performance.", "excitement": 3, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "I'm not aware of the literature in this area.", "Typos_Grammar_Style_and_Presentation_Improvements": "Please use the standard acl submitting style guide which has line numbers in the future. "}, "extra_text": {}}, {"review_id": "xAIWIKoBEf", "reviewer": "Reviewer_KWYb", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "*General*: We thank the reviewers for their time and comments and we are happy that all reviewers agree about the soundness, reproducibility and other merits of the work. \n\nDetailed response:\n\n*Incremental*: Allowing ourselves to be argumentative - we actually believe that the contribution is more that incremental, in spite of the integration of borrowed techniques. We believe that the combination of the techniques is unique, and that the overall results pave the way to further research.  \n\n*Model design and task*: This is a very good point! On the one hand - we did designed the model with a complex discourse parsing task in mind. On the other hand, we believe that the relation between the architecture and specific task, especially compared to other dialogue-related tasks actually warrants a full paper (that we will be happy to write or read). \n\n*Case-study*: Indeed! Unfortunately we reached the page limit. We plan to add this in the camera ready version if space permits.", "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": "1) The paper is well-written and easy to read.  \n\n2) The experimental results of the proposed methods including several variants are comprehensive. ", "reasons_to_reject": "1)The contribution of the paper is incremental. Some techniques mentioned in the paper, E.g., GRN, Asymmetric Loss, are adapted from other research directions.  \n\n2) The specific relation between the design of the model and the Non-Convergent Discourse Parsing task needs to be clarified further.  \n\n3) It would be better if a case study could be included in the experimental part.  ", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper focuses on the analysis of contentious discussions that are commonplace in many online platforms. A unified model for Non-Convergent Discourse Parsing that does not require any additional input other than the previous dialog utterances is proposed. The paper claims that the model achieves results comparable with SOTA. ", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "In section 4.2.7, line 6, the term \"1-hot\" should be \"one-hot\". "}, "extra_text": {}}, {"review_id": "PAzFaHpEAE", "reviewer": "Reviewer_MyNg", "summary": "", "questions": "Question A: How do you decide the proper value of \\alpha at the end of 4.2.6?\nQuestion B: What is the standard deviation of 5-fold cross validation?\nNote: these two questions are well explained by the authors in the rebuttal stage.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "*General*: We thank the reviewers for their time and comments and we are happy that all reviewers agree about the soundness, reproducibility and other merits of the work. \n\nDetailed response:\n*General*: We thank the reviewers for their time and comments and we are happy that all reviewers agree about the soundness, reproducibility and other merits of the work. \n\nDetailed response:\n\n*Z-TF Baseline*: The Z-TF is naive in the sense that it ignores previous utterances, yet it establishes the baseline obtained by a strong text classifier (BERT). We actually implemented versions that naively accept previous utterances (with separators) when input length permits, or previous gold labels, these results are excluded due to space constraints and our choice to highlight other empirical aspects and results. The GRN ablation results were omitted for similar reasons.\nWe do provide results for various context length (Table 2) and discuss it and the contribution of the auxiliary task in Section 6.2. We report on the  gains obtained by the asymmetric-loss in Table 1.\n\n\n*Alpha values*: We naively experimented with some values and noticed that smaller alpha values resulted in fluctuations and in catastrophic forgetting. \n\n*X-val STD*: For convenience, we will reply to the question with the averages of the standard deviations: The N-CoDiP model's per-category 5-fold results (Table 1, first column) have a mean standard deviation of 0.169 with a nearly identical median (0.168). The per-label 5-fold results (Appendix A) have a mean standard deviation of  0.047 with a median of 0.044. \nIn both cases, there are no significant outliers in the 5-fold results.", "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": "The proposed model N-CoDip provides a practical solution for discourse parsing for non-convergent multi-party dialogues. It achieves comparable results with SOTA but requires less resources and is simpler.", "reasons_to_reject": "Baseline model Z-TF is too simple. After reading the original paper (Zakharov et al., 2021) that utilises Z-TF (lack of details of the Z-TF model in this paper), my understanding is that it only uses the current utterance for classification. It will be better to try more advanced baselines that leverage both the context (previous utterances) and the current utterance. This context-aware baseline is essential to prove that your model componets are necessary, including: Gated Residual Networks (GRN), Label embedding, Auxiliary NMP task.", "questions_to_authors": "Question A: How do you decide the proper value of \\alpha at the end of 4.2.6?\nQuestion B: What is the standard deviation of 5-fold cross validation?\nNote: these two questions are well explained by the authors in the rebuttal stage.", "paper_topic_and_main_contributions": "The author(s) proposed a new model N-CoDiP for Non-Convergent Discourse Parsing for contentious dialogues, which achieved comparable results with the SOTA on Change My View (CMV) dataset. It also offers the following benefits compared to SOTA: 1) no feature engineering 2) a unified model instead of separate models for different labels 3) no requirement of gold labels of previous utterances.\n\nThe task was framed as an utterance-level multi-label classification task, where each utterance will be assigned one or more labels (e.g., Aggresive, No Reason Disagrrment). The dataset - Change My View (CMV) data, is taken from a previous paper. It contains multi-party dialogues constructed based on \"ChangeMyView\" subreddit with utterance-level annotations.\n\nThe proposed model N-CoDiP utilizes pre-trained transformers to get the embeddings of the current utterance and previous context, then aggregate the representations along with the embeddings of the label of the previous utterance using Gated Residual Networks (GRN). Asymmetric Loss was adopted to handle the imbalance of class labels.\n\nThe authors also explored the effectiveness of one-hot speaker representation, pretrained model enhanced with Contrastive Sentence Embedding and the auxiliary next message prediction task.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "It would be better to make the labels smaller and use a different style compared to utterances in Figure 1 to make the following aspects clear: 1) the discrimination between utterances and labels; 2) the correspondence between utterances and labels"}}, {"review_id": "ROtCC5byGT", "reviewer": "Reviewer_brxV", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "*General*: We thank the reviewers for their time and comments and we are happy that all reviewers agree about the soundness, reproducibility and other merits of the work. \n\nDetailed response:\n\n*Improving SOTA*: The model we propose should not be compared to SOTA only in terms of the numbers. Rather, it is a completely different approach that is not only more efficient and elegant - it does not require an ORACLE “leaking” information (gold labels of context) to the classifier. \n\n*Data*: In the paper we have demonstrated the applicability of the model to this complex task, advocating for the annotation of more data (under process), the promise in this line of research, and the benefits for scholars in other disciplines. \n\n*Annotation protocol*: We used the annotation scheme and data that were developed and provided by Zakharov et al., 2021.The tagset and the rationale behind it, the annotation protocol, and the dataset are presented  at length in Zakhariv et al. and in the accompanying repository (URL in footnote 4 in their paper). In our submission we only provide the necessary background. The reader is referred to the original paper and report by Zaharov for the finer details.", "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": "- The task is very complicated and there are not so much data for it.\n- the other conduct correctly their experiment and they have fair enough limitation remarks.\n", "reasons_to_reject": "- yet another experiment which slightly improve sota\n- Even if the limitation are stated, they are extremely important: the lack of data lead to a non reusable model.\n- annotation system w-should be more discussed and adapted.\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "In this paper, the authors proposes a unified model to parse specific kind of discourse (non-convergent ones), based on a fine-tuning of ROBERTa.\nThey achieve State of the art results, with limited informations.\n", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This work proposes an autoregressive method for Non-convergent discourse parsing. The approach and method are interesting, the paper's clarity is notable, and experimentation is on topic and shows promising results. As noted by the reviewers, the main issue is that the method could be evaluated in a more general setting (not just Non-convergent discourse). Therefore, it might have a more significant impact or be described in a larger context than the current version. However, the current version could be published and be a starting point to study the potential and limitations of the proposed approach.

Pros
* Interesting problem and method
* Promising results
* It is a self-contained manuscript

Cons
* Topic very specific but applicability to a larger scope
* Small dataset
Decision: Accept-Main