CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations
Authors: Divyaksh Shukla, Ritesh Baviskar, Dwijesh Gohil, Atul Shree, Ashutosh Modi
Accepted at: acl25

CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse parsing in conversations

Divyaksh Shukla*    Ritesh Baviskar*    Dwijesh Gohil°
Aniket Tiwari°    Atul Shree°    Ashutosh Modi*
*Indian Institute of Technology Kanpur (IIT Kanpur)    °Convin-AI
{divyaksh, ashutoshm}@cse.iitk.ac.in    atul@convin.ai

Abstract

Discourse parsing is an important task useful for NLU applications such as summarization, machine comprehension, and emotion recognition. The current discourse parsing datasets based on conversations consists of written English dialogues restricted to a single domain. In this resource paper, we introduce CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse parsing in conversations. The corpus (code-mixed in Hindi and English) has both audio and transcribed text and is annotated with nine discourse relations. We experiment with various SoTA baseline models; the poor performance of SoTA models highlights the challenges of multi-domain code-mixed corpus, pointing towards the need for developing better models for such realistic settings.

1 Introduction

Discourse structures (Mann and Thompson, 1988; Asher and Lascarides, 2005) capture relationships between clauses (e.g., causality, contrast, elaboration, and temporal sequencing) and are crucial to understanding the logical flow of information. These have been utilized in various tasks such as text summarization (Paulus et al., 2018; Li et al., 2016), language understanding, machine reading comprehension (Li et al., 2019), dialog generation (Chernyavskiy and Ilovovsky, 2023; Hassan and Alikhani, 2023; Chen and Yang, 2023) and emotion recognition (Zhang et al., 2023). Researchers have created annotated discourse corpora from human-to-human dialogues for a single language such as English (e.g., STAC (Asher et al., 2016) and Molweni (Li et al., 2020)). However, many modern-day conversations are audio-based and often involve code-mixing of multiple languages, such as Hindi and English (Hinglish). Understanding the discourse structure in such code-mixed audio-based conversations would be interesting. In this paper, we attempt to fill this gap. In a nutshell, we make the following contributions:
• We present CoMuMDR, a large scale code-mixed (Hindi + English = Hinglish), multi-modal (text+audio), multi-domain discourse corpus of two-party conversations (Table 1). CoMuMDR consists of audio recordings and corresponding transcriptions of customer call center interactions from multiple domains, including e-commerce, pharmaceutical, stock broker application support, e-marketplace, and education.
• The corpus is annotated to create a labeled discourse graph for link prediction and discourse relation classification. The annotation is done at the span level with nine discourse relation types that aptly support the flow of information in customer call centers. We merged a few relation types presented in SDRT (Asher and Lascarides, 2005) and added another type Question answer complaint pair to support the logical flow. Fig.

![An example of a two-party call-center conversation (in code-mixed language) regarding a complaint on product return. Utterances corresponding to the customer center representative are shown in green boxes, and those of the customer are in purple boxes. Here, the relation types of Question answer complaint pair and Acknowledgment are shortened to QACP and Ack, respectively. Here, dc is the correction by the annotator on incorrect diarization.](page_1047_320_370_370.png)

Figure 1: An example of a two-party call-center conversation (in code-mixed language) regarding a complaint on product return. Utterances corresponding to the customer center representative are shown in green boxes, and those of the customer are in purple boxes. Here, the relation types of Question answer complaint pair and Acknowledgment are shortened to QACP and Ack, respectively. Here, dc is the correction by the annotator on incorrect diarization.
<table>
  <tr>
    <th></th>
    <th>STAC</th>
    <th>Molweni</th>
    <th>CoMuMDR</th>
  </tr>
  <tr>
    <td># dialogues</td>
    <td>1137</td>
    <td>10000</td>
    <td>799</td>
  </tr>
  <tr>
    <td># utterances</td>
    <td>106788</td>
    <td>85042</td>
    <td>8811</td>
  </tr>
  <tr>
    <td># utterances/dialogue</td>
    <td>94.43</td>
    <td>85.01</td>
    <td>88.67</td>
  </tr>
  <tr>
    <td># words/dialogue</td>
    <td>11.07</td>
    <td>8.83</td>
    <td>11.03</td>
  </tr>
  <tr>
    <td>Parties</td>
    <td>Multi</td>
    <td>Multi</td>
    <td>Two</td>
  </tr>
  <tr>
    <td>Modalities</td>
    <td>Unimodal</td>
    <td>Unimodal</td>
    <td>Multi-modal</td>
  </tr>
  <tr>
    <td>Languages</td>
    <td>English</td>
    <td>English</td>
    <td>Code-mixed</td>
  </tr>
  <tr>
    <td>Source</td>
    <td>Catan Game</td>
    <td>Ubuntu chats</td>
    <td>Call center interactions</td>
  </tr>
  <tr>
    <td>Discourse Labels</td>
    <td>17 labels</td>
    <td>17 labels</td>
    <td>9 labels</td>
  </tr>
  <tr>
    <td>Metrics</td>
    <td>Precision</td>
    <td>Recall</td>
    <td>Kappa, Jaccard</td>
  </tr>
  <tr>
    <td>Data split # dialogues</td>
    <td>Train 909</td>
    <td>9000</td>
    <td>670</td>
  </tr>
  <tr>
    <td></td>
    <td>Test 115</td>
    <td>500</td>
    <td>81</td>
  </tr>
  <tr>
    <td></td>
    <td>Validation 113</td>
    <td>500</td>
    <td>79</td>
  </tr>
</table>

Table 1: Comparison with previous corpora

1 shows a sample for CoMuMDR. The conversations in a practical setting can be complex; for example, there can be an overlap (§3) between utterances (7th utterance) of two speakers. Also, note that since we used ASR and a diarization model for transcribing and splitting (§3), an utterance (e.g., utterances 1, 2, and 3) could get incorrectly split due to diarization errors. These are resolved during annotations (§3).

• We evaluate existing text-based discourse parsers (and GPT-4o) for link and relation prediction on CoMuMDR using English-only and multilingual text embeddings. We compare this with the performance of existing corpora, STAC and Molweni. We observe that SoTA models underperformed on CoMuMDR, pointing towards the need for the development of advanced models.

• We will release the experiment code, audio transcriptions (and text embeddings), and audio features via GitHub: https://github.com/Exploration-Lab/CoMuMDR. We do not release the actual audio and unfiltered transcripts due to concerns about the privacy of the company and its customers.

The motivation behind CoMuMDR is to create a practical, real-world system that handles audio conversations and is robust to transcription and diarization errors.

2 Related Work

Discourse Parsing has been an active research area in the NLP community (Li et al., 2022). Discourse parsing consists of three main components: discourse segmentation (Wang et al., 2018; Lukasik et al., 2020; Liu et al., 2021), discourse link prediction and discourse relation classification. Discourse segmentation divides a text corpus into Elementary Discourse Units (EDUs) for further processing. Discourse link prediction predicts a directed link between two EDUs, and relation classification assigns a relation type to the link (also check discourse theories in App. A.1).

Datasets: In the context of English, two main text-based corpora have been proposed for Discourse parsing: STAC (Asher et al., 2016) and Molweni (Li et al., 2020) (check details in App. A.2). Table 1 compares the STAC and Molweni datasets with our proposed dataset. CoMuMDR is code-mixed, audio-based, and covers multiple domains as opposed to mono-lingual single-domain conversations covered by existing text-based datasets. The corpus (having a comparable number of words with STAC) is based on Hindi-English code-mixed audio conversations with imperfect transcription and diarization quality, so CoMuMDR proposes a practical outlook on discourse parsing in conversations. Note that compared to existing datasets (STAC (based on the Catan game) and Molweni (based on Ubuntu chats)), CoMuMDR, besides including audio information, covers more domains and a variety of topics.

Discourse Parsing Models: Various approaches have been proposed for Discourse parsing such as deep sequential model (Shi and Huang, 2019), hierarchical model (Liu and Chen, 2021), Structure-aware model (Wang et al., 2021), SSBERT+SCIJE model by Yu et al. (2022) and SDDP model by (Chi and Rudnicky, 2022). Due to space constraints, details are given in App. A.3. We benchmark using each of the above models.

Figure 2: Distribution of discourse labels in CoMuMDR.

![Distribution of discourse labels in CoMuMDR.](page_1012_120_388_246.png)

3 CoMuMDR Creation

CoMuMDR consists of two-party customer call center interactions. We obtained the data via a joint research collaboration with a call center company (they own the data) and want to automate customer call understanding. The calls mainly cater to Indian customers and companies. We ensure that the privacy of customers and companies mentioned in a call is maintained during annotation. The audio data is transcribed using the existing ASR (Automatic Speech Recognition) system (Verma et al., 2023) (details in App. B.1) and diarized into utterances (Koluguri et al., 2021) (details in App. B.2). Subsequently, the data is anonymized for customer names and other private information. A team of
three professional annotators further annotated the transcribed and diarized data.

Discourse Labels: We used nine discourse labels: Acknowledgment, Question-Answer pair, Question-Answer Complaint pair, Background, Contrast, Correction, Question Extension, Conditional, and Continuation. App. Table 6 lists the discourse labels (and definitions). The labels are based on Semantic Discourse Representation Theory (SDRT) (Asher et al., 2016). App. G provides details of the distribution of relative distance between linked EDU pairs for each relationship type (discourse label). In addition, we propose a new relation type, Question-Answer Complaint Pair, to classify complaints separately. We removed the Narration discourse label as it was not required in two-party customer conversations. During a pilot annotation experiment, we found that several discourse labels conveyed overlapping meanings. Hence, we merged them to get nine discourse labels to annotate our data (see Fig. 2). We observe a shift in the distribution of the labels when compared with STAC and Molweni. Specifically, CoMuMDR has a lower frequency of Continuation relation and a higher frequency of Question Answer pair and Acknowledgment relations, which is typical of customer call center interactions (see App. Fig. 4).

Question Extension label is arrived by merging Clarification Question and Question Elaboration, as all the instances of customer call center conversations posed clarification questions as elaborations, and the answers to them were more akin to answers to elaborative questions. Conditional is made by merging Alternation and Conditional as due to the nature of Hindi-English code-mixed conversations, it is hard to differentiate between a conditional and an alternation. Continuation is made by merging Comment, Elaboration, Parallel and Result since call center conversations rarely contain examples of comments compared to STAC and Molweni. Customer calls do not revolve around multiple ideas or topics; hence, there is no notion of parallel. Customer call center representatives continuously assure the customers about quick resolution of complaints, and the result of the conversation is typically implicit.

Dialogues are divided into utterances using a diarization model. However, the audio contains overlapping utterances where both speakers are speaking simultaneously. Hence, we added another relation termed Diarization Continuation to fix the diarization issues. However, this relation label is not part of the discourse and is not used in calculating the results (§4). We plan to use these annotations to improve the diarization model.

Annotation Details: Annotators were tasked to identify the Elementary Discourse Units (EDUs), predict links between them to form a DAG, and assign a label to the relation between EDUs. A team of two annotators independently annotated the data, and another annotator verified the annotations and marked batches for re-annotation if deemed necessary. Previous studies have shown that identifying Complex Discourse Units (CDUs)\footnote{Multiple EDUs are combined using discourse relations to form a Complex Discourse Unit (CDU), making a discourse tree structure.} is a challenging task and have relied on combining EDUs using discourse relations to get a CDU (Muller et al., 2012; Afantenos et al., 2015). Prior discourse parsing models have used various strategies to convert CDUs to EDUs for efficient parsing (Shi and Huang, 2019; Liu and Chen, 2021). We identified similar challenges in annotation and hence, instructed the annotators to connect an EDU with only the head (i.e., the first EDU) of a CDU (Asher et al., 2016). Appendix C presents more details regarding the annotation. We used the Inception software (Kie et al., 2018) for annotation (\S C.1). We provide details of annotators, instructions, and processes in the App. C.2, App. C.3, and App. C.4, respectively.

Inter-Annotator Agreement: Table 2 shows inter-annotator agreement using various metrics. Given our complex setting, existing metrics (e.g., Kappa) show a relatively low performance compared to previous datasets. We computed Kappa (McHugh, 2012) using the span and relation exact match metrics as in STAC and Molweni (Asher et al., 2016; Li et al., 2020).

<table>
  <tr>
    <th>Metric</th>
    <th>Score</th>
  </tr>
  <tr>
    <td>Jaccard</td>
    <td>0.9569</td>
  </tr>
  <tr>
    <td>Span exact match (see App. C.5)</td>
    <td>0.6294</td>
  </tr>
  <tr>
    <td>Span partial match (see App. C.5)</td>
    <td>0.8224</td>
  </tr>
  <tr>
    <td>Relation exact match (see App. C.5)</td>
    <td>0.5800</td>
  </tr>
  <tr>
    <td>Relation partial match (see App. C.5)</td>
    <td>0.8721</td>
  </tr>
  <tr>
    <td>Structured Kappa (Asher et al., 2016; Li et al., 2020)</td>
    <td>0.4044</td>
  </tr>
  <tr>
    <td>Relationship Kappa (Asher et al., 2016; Li et al., 2020)</td>
    <td>0.3190</td>
  </tr>
</table>

Table 2: Inter-annotator agreement metrics

4 Experiments, Results and Analysis

Discourse Modeling: A dialogue consists of a list of utterances between two speakers. The utter-
<table>
  <tr>
    <th rowspan="2">STAC</th>
    <th colspan="3">Link only</th>
    <th colspan="3">Link + relation</th>
  </tr>
  <tr>
    <th>Molweni</th>
    <th>CoMuMDR</th>
    <th>STAC</th>
    <th>Molweni</th>
    <th>CoMuMDR</th>
  </tr>
  <tr>
    <td>Hierarchical</td>
    <td>0.6841</td>
    <td>0.7000</td>
    <td>0.9036</td>
    <td>0.5211</td>
    <td>0.5733</td>
    <td>0.4263</td>
  </tr>
  <tr>
    <td>Structure-aware</td>
    <td>0.7125</td>
    <td>0.8056</td>
    <td>0.9255</td>
    <td>0.5314</td>
    <td>0.5614</td>
    <td>0.4850</td>
  </tr>
  <tr>
    <td>SSP-BERT + SCIJE</td>
    <td>0.7250</td>
    <td>0.8085</td>
    <td>0.9251</td>
    <td>0.6151</td>
    <td>0.6634</td>
    <td>0.5547</td>
  </tr>
  <tr>
    <td>SDDP</td>
    <td>0.7304</td>
    <td>0.7898</td>
    <td>0.9416</td>
    <td>0.5670</td>
    <td>0.5770</td>
    <td>0.3781</td>
  </tr>
  <tr>
    <td colspan="7">English-only embeddings</td>
  </tr>
  <tr>
    <td>Deep Sequential</td>
    <td>0.7496</td>
    <td>0.7577</td>
    <td>0.7330</td>
    <td>0.6318</td>
    <td>0.5162</td>
    <td>0.4796</td>
  </tr>
  <tr>
    <td>Hierarchical</td>
    <td>0.7505</td>
    <td>0.8097</td>
    <td>0.9443</td>
    <td>0.5704</td>
    <td>0.5690</td>
    <td>0.5786</td>
  </tr>
  <tr>
    <td>Structure-aware</td>
    <td>0.7267</td>
    <td>0.8232</td>
    <td>0.7782</td>
    <td>0.5582</td>
    <td>0.5934</td>
    <td>0.4072</td>
  </tr>
  <tr>
    <td>SSP-BERT + SCIJE</td>
    <td>0.7201</td>
    <td>0.8293</td>
    <td>0.9452</td>
    <td>0.5623</td>
    <td>0.5925</td>
    <td>0.5675</td>
  </tr>
  <tr>
    <td>SDDP</td>
    <td>0.7488</td>
    <td>0.8233</td>
    <td>0.7918</td>
    <td>0.5887</td>
    <td>0.5770</td>
    <td>0.2941</td>
  </tr>
</table>

Table 3: F1-score of various discourse parsing models. Values in bold highlight the top-performing model in each method, while values in underline highlight the next top-performing model.

ances are further divided into elementary discourse units (i.e., clauses (Asher and Lascarides, 2005)) \( \{u_0, u_1, ..., u_n\} \), where \( u_0 \) is a dummy root EDU. Discourse parsing involves predicting a directed link between two EDUs \( u_j \) and \( u_i \) and assigning a relation label \( r_{ji} \) between EDUs \( u_j \) and \( u_i \).

Experimental Setup: We experimented with state-of-the-art discourse parsing models: deep sequential model (Shi and Huang, 2019), hierarchical model (Liu and Chen, 2021), Structure-aware model (Wang et al., 2021), SSP-BERT+SCIJE model (Yu et al., 2022) and SDDP model (Chi and Rudnicky, 2022). We implemented all the discourse parsing models on STAC, Molweni, and CoMuMDR and trained them from scratch (details in App. D). We followed the data-split (train/validation/test) as given in Table 1. Validation set was used to tune the models. We implemented the models in two settings depending how texts in EDUs are encoded: English-only and multilingual embeddings. English-only embeddings include GLoVe (Pennington et al., 2014) or Roberta-base embeddings (Liu et al., 2019), same as those used in the original implementations. On the other hand, multilingual sentence-level embeddings include paraphrase-xlm-r-multilingual-v1 (Reimers and Gurevych, 2019), which convert a complete EDU’s text to a 768-dimension vector.

Results: Table 3 shows the F1-score (App. E) on test sets for link and link+relation prediction. For both the settings, CoMuMDR scores are lowest across all the models, highlighting the challenge of discourse parsing on multi-domain, multilingual conversations. As can be observed, CoMuMDR has an equivalent score across models for link prediction. SDDP and Hierarchical model outperform on CoMuMDR compared to STAC and Molweni. However, in relation classification (link+relation),

![Line plot comparing F1-score across epochs for different models and methods](page_1012_120_410_320.png)

Figure 3: Comparing SDDP, Struct-Aware, and SA-ADPM on epochs on CoMuMDR test set. The values on each plot indicate the highest F1 score with respect to each model and discourse parsing method.

CoMuMDR has the lowest performance, possibly due to the presence of multiple domains and the challenge of domain adaptation (Liu and Chen, 2021).

Error Analysis: In Table 3, we have computed the results of link and link+relation prediction of SDDP using Roberta and paraphrase-xlm-r-multilingual-v1. Roberta handles English-only text and cannot handle code-mixed or Hindi text. On the other hand, paraphrase-xlm-r-multilingual-v1 can handle multilingual text but often fails at effectively processing code-mixed text. Hence, there is lower performance on relation prediction for SDDP. Baseline methods, including Deep sequential, hierarchical, Structure-aware, SSP-Bert + SCIJE, perform relation prediction after link prediction, i.e., they classify the relation type for each predicted link. On the other hand, SDDP performs link+relation prediction simultaneously as a single task, which is much more complicated. Hence, SDDP shows significantly lower performance on link+relation prediction than other baseline methods. Additionally, SDDP assumes the discourse relations to form a tree and performs tree parsing during inference, while most of the discourse relations in CoMuMDR cannot adhere to tree structures. Hence, SDDP on CoMuMDR shows low scores on link+relation prediction.

We further analyzed the learning dynamics of the baseline models on CoMuMDR and present results in Fig. 3. The utterances were encoded using paraphrase-xlm-r-multilingual-v1 while training all models on CoMuMDR. For the structure-
<table>
  <tr>
    <th rowspan="2">Label</th>
    <th colspan="3">Hierarchical</th>
    <th colspan="3">Struct-Aware</th>
    <th colspan="3">SSP-BERT + SCIJE</th>
    <th colspan="3">SDDP</th>
  </tr>
  <tr>
    <th>Precision</th>
    <th>Recall</th>
    <th>F1-score</th>
    <th>Precision</th>
    <th>Recall</th>
    <th>F1-score</th>
    <th>Precision</th>
    <th>Recall</th>
    <th>F1-score</th>
    <th>Precision</th>
    <th>Recall</th>
    <th>F1-score</th>
    <th>Support</th>
  </tr>
  <tr>
    <td>Question Extension</td>
    <td>0.39</td>
    <td>0.47</td>
    <td>0.42</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.57</td>
    <td>0.61</td>
    <td>0.59</td>
    <td>0.18</td>
    <td>0.30</td>
    <td>0.22</td>
    <td>172</td>
  </tr>
  <tr>
    <td>Acknowledgment</td>
    <td>0.62</td>
    <td>0.68</td>
    <td><b>0.65</b></td>
    <td>0.56</td>
    <td>0.47</td>
    <td><b>0.51</b></td>
    <td>0.68</td>
    <td>0.72</td>
    <td><b>0.70</b></td>
    <td>0.04</td>
    <td>0.50</td>
    <td>0.08</td>
    <td>151</td>
  </tr>
  <tr>
    <td>Question Answer</td>
    <td>0.54</td>
    <td>0.58</td>
    <td>0.56</td>
    <td>0.40</td>
    <td>0.66</td>
    <td>0.49</td>
    <td>0.53</td>
    <td>0.45</td>
    <td>0.49</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>146</td>
  </tr>
  <tr>
    <td>Continuation</td>
    <td>0.38</td>
    <td>0.43</td>
    <td>0.40</td>
    <td>0.31</td>
    <td>0.44</td>
    <td>0.35</td>
    <td>0.38</td>
    <td>0.47</td>
    <td>0.42</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>109</td>
  </tr>
  <tr>
    <td>Background</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.28</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>1.00</td>
    <td>0.11</td>
    <td>0.20</td>
    <td>32</td>
  </tr>
  <tr>
    <td>Contrast</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.17</td>
    <td>0.08</td>
    <td>0.11</td>
    <td>0.50</td>
    <td>0.44</td>
    <td>0.47</td>
    <td>0.40</td>
    <td>0.39</td>
    <td><b>0.39</b></td>
    <td>8</td>
  </tr>
  <tr>
    <td>Conditional</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.14</td>
    <td>0.07</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>6</td>
  </tr>
  <tr>
    <td>Question Answer Complaint</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>3</td>
  </tr>
  <tr>
    <td>Correction</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>2</td>
  </tr>
</table>

Table 4: Comparing precision, recall and F1 scores of predicted relations across three baseline discourse parsing models with multi-lingual embeddings on the test set of CoMuMDR.

aware and SSP-BERT + SCIJE models, we observed a rapid increase in F1-scores for link prediction, suggesting that the learned representations generalize well just after a single epoch. Notably, the F1-score for the struct-aware model remains nearly constant over 10 epochs, indicating early saturation in learning for link prediction. In contrast, link+relation prediction requires multiple epochs to generalize, and the corresponding scores exhibit high variance across epochs. This suggests that while the model may be well-suited for link prediction, it struggles to maintain stability and consistency for the more complex link+relation task.

Despite achieving notable improvements in link prediction on CoMuMDR using multilingual embeddings, we observe only a marginal increase in link+relation prediction scores. Table 4 provides a breakdown of precision, recall, and F1-scores for relation type prediction across SSP-BERT + SCIJE, structure-aware, and SDDP models. These scores reflect the underlying relation distribution shown in Fig. 2. Relations with low support counts are not effectively learned—even when using a weighted loss function, where the weights are computed as the inverse of their support counts. This is evident from the zero F1-scores for several rare relation types across all three models. For example, struct-aware achieves an F1-score of zero on the Question Extension relation, which contributes to its reduced overall performance on CoMuMDR despite strong results on link prediction. Overall, SSP-BERT + SCIJE outperforms the other baselines on CoMuMDR, but still falls short in predicting rare relations such as Conditional, Question answer complaint, and Correction. On further analysis, we observed that the hierarchical model (Liu and Chen, 2021) could not predict the same relation links for Correction and Contrast on CoMuMDR, leading to a loss of performance in link prediction and relation classification involving correction and contrast (see Table 4). The hierarchical model easily identifies Acknowledgment relations among the correctly predicted links. It could be due to the strong presence (~ 18%) of Acknowledgment in the dataset. Similarly, in SSP-BERT, the model misclassified some Acknowledgment relations as Question answer pairs. App. Fig. 5 is an example of a conversation snippet with the gold and predicted relations marked on the left and right sides, respectively. The model incorrectly classified an Acknowledgment relation as a Question Answer pair, possibly due to the presence of “ma’am” in the acknowledgment clause (also see App. Fig. 5).

Results of GPT-4 Model: We evaluated GPT-4o on the test set (81 dialogues, 890 utterances). We prompted GPT-4o in a 3-shot setting (template in App. F) to behave as a discourse parser (results in Table 5). GPT-4o performs worse on both tasks compared to the SoTA models. Upon examining the confusion matrix (App. Table 9) for GPT-4o on CoMuMDR, we observed the misclassifications of Question extension as Continuation, possibly due to the overlapping semantics of these relations in a two-party conversation.

<table>
  <tr>
    <th></th>
    <th>Link only</th>
    <th>Link+relation</th>
  </tr>
  <tr>
    <td>STAC</td>
    <td>0.6012</td>
    <td>0.2729</td>
  </tr>
  <tr>
    <td>Molweni</td>
    <td>0.5176</td>
    <td>0.1474</td>
  </tr>
  <tr>
    <td>CoMuMDR</td>
    <td><b>0.7217</b></td>
    <td><b>0.2808</b></td>
  </tr>
</table>

Table 5: Performance of GPT-4o as a discourse parser.

5 Future Directions and Conclusion

This paper presents CoMuMDR, a new discourse corpus for multi-modal, multi-domain, and code-mixed conversations from various customer call centers. We transcribed the audio and diarized the text into utterances. We annotated the EDUs using nine discourse labels by combining a few closely related labels from the SDRT format as they formed a more appropriate flow of discourse in a two-party conversation on customer support calls. In this work, we experimented with SoTA models; however, these do not perform well on CoMuMDR. In the future, we plan to develop more advanced models incorporating audio modality information.
Limitations

We developed CoMuMDR by capturing audio conversations between a customer and a customer care representative. The audio is then transcribed for annotation.

Our corpus is not as big as the existing Discourse corpora but our corpus is code-mixed, multi-domain, and multi-modal. The corpus is sizable enough to develop meaningful models. Nevertheless, we plan to keep growing our corpus. Discourse annotations is a very time consuming process and hence it takes time to expand the corpus.

CoMuMDR consists of nine discourse relation labels, far fewer than STAC and Molweni, which contain 17 labels. We found during our pilot annotation process that the Narration discourse label had no role in customer-centered conversations. Also, we found that in two-party conversations, some of the discourse labels had quite confusing meanings, which led to poor inter-annotator agreements. Hence, we combined the labels to create our presented nine labels presented in Table 6.

To build the dataset, we collected audio recordings from customer care centers. The audio was then transcribed and diarized. We found that the state-of-the-art diarization model gave imperfect diarizations during our pilot annotation process. It is because the audio data we collected consists of overlapping audio, i.e., both speakers are speaking simultaneously, and the transcription model returns text for both speakers. Hence, we added another annotation termed diarization continuation, and the annotators were tasked to fix the diarization issues along with discourse relation annotation.

The RST and SDRT theories (Mann and Thompson, 1988; Asher and Lascarides, 2005) define clauses as the textual span to be used as elementary discourse units (EDU). However, due to the nature of CoMuMDR and the imperfect diarizations resulting from the same, we could not use off-the-shelf clause identification algorithms. Hence, our annotation effort also includes the manual identification of EDUs and discourse relation annotation. It led to annotator-level differences in selecting clause spans. Hence, we report different annotation metrics in Appendix C.

Ethical Considerations

CoMuMDR is constructed by obtaining audio conversation data from customer call center offices. The data is obtained under the agreement between us and the research collaborator (call center company). All the data that was used for experimentation complies with the terms of use and licensing agreements.

The audio transcriptions in CoMuMDR are anonymized for of all personally identifiable information. We also removed instances of toxic language, offensive or harmful content, and sensitive or wrong information from CoMuMDR.

CoMuMDR consists of Hindi-English code-mixed conversations taken from a specific geographical section. The data contains conversations from companies in pharmaceutical, e-commerce, stock broker applications, e-marketplaces, and education counseling services.

We made sure to remove any bias in the data. Any bias, toxic language, offensive or harmful content, sensitive information, and misinformation in CoMuMDR is entirely unintentional.

Due to licensing agreements and ethical constraints, we will not be releasing the original audio data in CoMuMDR. We will only release the anonymized text transcriptions, corresponding text embeddings and audio features along with appropriate annotations in CoMuMDR.

References

Stergos Afantenos, Eric Kow, Nicholas Asher, and Jérémy Perret. 2015. Discourse parsing for multiparty chat dialogues. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 928–937, Lisbon, Portugal. Association for Computational Linguistics.

Nicholas Asher, Julie Hunter, Mathieu Morey, Benamara Farah, and Stergos Afantenos. 2016. Discourse structure and dialogue acts in multiparty dialogue: the STAC corpus. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 2721–2727, Portorož, Slovenia. European Language Resources Association (ELRA).

Nicholas Asher and Alex Lascarides. 2005. Logics of Conversation. Cambridge University Press, Cambridge, England, UK.

Jiaoo Chen and Diyi Yang. 2023. Controllable conversation generation with conversation structures via diffusion models. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7238–7251, Toronto, Canada. Association for Computational Linguistics.

Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long
Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Xiangzhan Yu, and Furui Wei. 2022. Wavlm: Large-scale self-supervised pre-training for full stack speech processing. IEEE Journal of Selected Topics in Signal Processing, 16(6):1505–1518.

Alexander Chernyavskiy and Dmitry Ilvovsky. 2023. Transformer-based multi-party conversation generation using dialogue discourse acts planning. In Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 519–529, Prague, Czechia. Association for Computational Linguistics.

Ta-Chung Chi and Alexander Rudnicky. 2022. Structured dialogue discourse parsing. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 325–335, Edinburgh, UK. Association for Computational Linguistics.

Sabit Hassan and Malihe Alikhani. 2023. DisCGen: A framework for discourse-informed counterspeech generation. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 420–429, Nusa Dua, Bali. Association for Computational Linguistics.

Kenneth Heafield. 2011. KenLM: Faster and smaller language model queries. In Proceedings of the Sixth Workshop on Statistical Machine Translation, pages 187–197, Edinburgh, Scotland. Association for Computational Linguistics.

Jan-Christoph Klie, Michael Bugert, Beto Boullosa, Richard Eckart de Castilho, and Iryna Gurevych. 2018. The INCEption platform: Machine-assisted and knowledge-oriented interactive annotation. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 5–9, Santa Fe, New Mexico. Association for Computational Linguistics.

Nithin Rao Koluguri, Taejin Park, and Boris Ginsburg. 2021. Titanet: Neural model for speaker representation with 1d depth-wise separable convolutions and global context. Preprint, arXiv:2110.04410.

Jiaqi Li, Ming Liu, Min-Yen Kan, Zihao Zheng, Zekun Wang, Wengqiang Lei, Ting Liu, and Bing Qin. 2020. Molweni: A challenge multiparty dialogues-based machine reading comprehension dataset with discourse structure. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2642–2652, Barcelona, Spain (Online). International Committee on Computational Linguistics.

Jiaqi Li, Ming Liu, Bing Qin, and Ting Liu. 2022. A survey of discourse parsing. Frontiers of Computer Science, 16(5):165329.

Jiaqi Li, Ming Liu, Bing Qin, Zihao Zheng, and Ting Liu. 2019. An annotation scheme of a large-scale multi-party dialogues dataset for discourse parsing and machine comprehension. Preprint, arXiv:1911.03514.

Junyi Jessy Li, Kapil Thadani, and Amanda Stent. 2016. The role of discourse units in near-extractive summarization. In Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 137–147, Los Angeles. Association for Computational Linguistics.

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. CoRR, abs/1907.11692.

Zhengyuan Liu and Nancy Chen. 2021. Improving multi-party dialogue discourse parsing via domain integration. In Proceedings of the 2nd Workshop on Computational Approaches to Discourse, pages 122–127, Punta Cana, Dominican Republic and Online. Association for Computational Linguistics.

Zhengyuan Liu, Ke Shi, and Nancy Chen. 2021. DMRST: A joint framework for document-level multilingual RST discourse segmentation and parsing. In Proceedings of the 2nd Workshop on Computational Approaches to Discourse, pages 154–164, Punta Cana, Dominican Republic and Online. Association for Computational Linguistics.

Michal Lukasik, Boris Dadachev, Kishore Papineni, and Gonçalo Simões. 2020. Text segmentation by cross segment attention. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4707–4716, Online. Association for Computational Linguistics.

William C. Mann and Sandra A. Thompson. 1988. Rhetorical structure theory: Toward a functional theory of text organization. Text - Interdisciplinary Journal for the Study of Discourse, 8(3):243–281.

Mary L. McHugh. 2012. Interrater reliability: the kappa statistic. Biochemia Medica, 22(3):276.

Philippe Muller, Stergos Afantenos, Pascal Denis, and Nicholas Asher. 2012. Constrained decoding for text-level discourse parsing. In Proceedings of COLING 2012, pages 1883–1900, Mumbai, India. The COLING 2012 Organizing Committee.

Romain Paulus, Caiming Xiong, and Richard Socher. 2018. A deep reinforced model for abstractive summarization. In International Conference on Learning Representations.

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, Doha, Qatar. Association for Computational Linguistics.
Alan Lee Rashmi Prasad, Bonnie Webber and Aravind Joshi. 2019. Penn Discourse Treebank Version 3.0 - Linguistic Data Consortium.

Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982–3992, Hong Kong, China. Association for Computational Linguistics.

Zhouxing Shi and Minlie Huang. 2019. A deep sequential model for discourse parsing on multi-party dialogues. In AAAI 2019, AAAI’19/IAAI’19/EAAI’19. AAAI Press.

Tushar Verma, Atul Shree, and Ashutosh Modi. 2023. Asr for low resource and multilingual noisy code-mixed speech. In INTERSPEECH 2023, pages 3242–3246.

Ante Wang, Linfeng Song, Hui Jiang, Shaopeng Lai, Junfeng Yao, Min Zhang, and Jinsong Su. 2021. A structure self-aware model for discourse parsing on multi-party dialogues. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 3943–3949. International Joint Conferences on Artificial Intelligence Organization. Main Track.

Yizhong Wang, Sujian Li, and Jingfeng Yang. 2018. Toward fast and accurate neural discourse segmentation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 962–967. Brussels, Belgium. Association for Computational Linguistics.

Chang-Sung Yu. 1986. Graph theory, by w. t. tutte, encyclopedia of mathematics and its applications, volume 21, addison-wesley publishing company, menlo park, ca., 1984, 333 pp. price: 45.00. Networks, 16(1):107–108.

Nan Yu, Guohong Fu, and Min Zhang. 2022. Speaker-aware discourse parsing on multi-party dialogues. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5372–5382, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.

Duzhen Zhang, Feilong Chen, and Xiuyi Chen. 2023. DualGATs: Dual graph attention networks for emotion recognition in conversations. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7395–7408, Toronto, Canada. Association for Computational Linguistics.
Appendix

Table of Contents

A  Related Work ..................... 10
    A.1  Discourse Parsing Theories . . 10
    A.2  Other Corpora ............... 10
    A.3  Previous Methods ............ 10
B  Corpus Creation .................. 11
    B.1  Automatic Speech Recognition (ASR) ............... 11
    B.2  Speaker Diarization .......... 11
C  Annotation details ............... 11
    C.1  Annotation Software ......... 11
    C.2  Annotator Profiles and Payment . 11
    C.3  Annotation Instructions ...... 11
    C.4  Annotation Process .......... 12
    C.5  Inter Annotator Agreement Metrics 12
D  Model Training Details .......... 12
E  Evaluation Metric ............... 12
F  GPT-4 Template .................. 12
G  Distance between linked EDUs ..... 12

List of Tables

<table>
  <tr><th>6</th><td>Discourse relation labels and descriptions</td><td>15</td></tr>
  <tr><th>7</th><td>Hyperparameter settings</td><td>16</td></tr>
  <tr><th>8</th><td>EDU distance distribution</td><td>16</td></tr>
  <tr><th>9</th><td>GPT-4o confusion matrix</td><td>16</td></tr>
</table>

List of Figures

<table>
  <tr><th>4</th><td>Discourse relation distribution</td><td>13</td></tr>
  <tr><th>5</th><td>CoMuMDR sample conversation</td><td>13</td></tr>
  <tr><th>6</th><td>GPT-4 prompt template</td><td>13</td></tr>
  <tr><th>7</th><td>Distance between linked EDUs for different corpora</td><td>14</td></tr>
</table>
A  Related Work

A.1 Discourse Parsing Theories
There are two prominent theories around discourse parsing and structures. The RST theory (Mann and Thompson, 1988) defines EDUs as clauses (made of subject, object, and predicate). EDUs are then linked to form a discourse tree. The Penn Discourse Treebank developed a parser to divide a text corpus into EDUs and establish relationships between them using the grammar from RST (Rashmi Prasad and Joshi, 2019). The Semantic Discourse Representation Theory (SDRT) realizes the need for discourse in AI-based tools dealing with discourse (Asher and Lascarides, 2005). SDRT defines the theoretical background of discourse relations. The relationship is driven by dynamic logical semantics and a discourse structure.

A.2 Other Corpora
STAC (Asher et al., 2016): The STAC corpus is built on the online game of “Settlers of Catan”. The game revolves around multiple players with dynamic resources to play and survive on a newly occupied land. Participants interact with each other on a chat system. The interaction includes gameplay interactions and general conversations. Hence, one can replay the entire game by noting the chat interactions. The STAC corpus is built on the recordings of the chat interface and hence includes gameplay-related interactions and general conversations. Asher et al. (2016) used the SDRT discourse theory to annotate 17 relation types between EDUs.

Molweni (Li et al., 2020): The Molweni dataset is based on Ubuntu support chat. This is a multiparty chat environment and is domain-specific. The annotation is based on the SDRT discourse theory and contains 17 relation types between EDUs.

Table 1 compares the STAC and Molweni datasets with our proposed dataset. CoMuMDR is built by transcribing audio call interactions between a customer and a call center representative. We sourced data from multiple customer call centers catering to domains, including e-commerce, pharmaceutical, stock broker application support, e-marketplace, and education counseling. On the other hand, STAC and Molweni datasets consist of single domains, namely Catan conversations and Ubuntu support. CoMuMDR is built from Hindi-English code-mixed audio conversations with imperfect transcription and diarization quality, imposing a practical outlook on discourse parsing in conversations.

A.3 Previous Methods
Deep Sequential (Shi and Huang, 2019): develops non-structured and structured EDU representations for jointly optimizing link prediction and relation classification. The model sequentially predicts the link and classifies relations for each EDU in a dialog. Glove embeddings are taken for tokens in the EDU and used for downstream models.

Hierarchical (Liu and Chen, 2021): The authors employ a hierarchical text embeddings approach by first encoding the text using a transformer followed by a BiGRU layer to compute EDU representations. Links are predicted by concatenating the representations of an EDU with all the previous EDUs and passing them through a linear layer. A discourse relation is classified by concatenating the representations of two connected EDUs. The authors experiment on STAC and Molweni datasets and highlight a need for domain adaptive models. Since STAC and Molweni are single-domain datasets, they are ineffective in training a model for cross-domain discourse parsing.

Structure-aware (Wang et al., 2021) jointly optimizes link and relation prediction. The EDUs are passed through a Hierarchical GRU to obtain context-aware dialog-level embeddings. This is then passed through a GNN containing a structure-aware dot product attention module to compute relation embeddings. As a discourse graph is a DAG, the relation embeddings here are computed for the forward and backward directions. These relation embeddings are then used for link prediction and relation classification.

SSP-BERT+SCIJE (Yu et al., 2022): The authors finetune a BERT model to predict if 2 EDUs have the same speaker, which is termed as SSP-BERT. The model then concatenates the embeddings of different speakers and the same speaker using a standard BERT and SSP-BERT model to predict links and classify discourse relation labels jointly.

SDDP (Chi and Rudnicky, 2022): This model jointly optimizes link and relation prediction on tree-level distributions. They discard a fraction of the edges to convert the discourse graph from a directed-acyclic graph (DAG) to a minimum spanning tree (MST) to efficiently learn and decode the discourse structure. The discourse tree is learned by minimizing the KL divergence between the predicted and reference tree distributions. The proba-
bility distribution of the tree is calculated by computing a tree’s score and dividing it by the score of all possible tree structures, i.e., the partition function. The partition function is approximated using the Matrix-Tree theorem (Yu, 1986).

B Corpus Creation

B.1 Automatic Speech Recognition (ASR)
Our ASR system leverages the WavLM model (Chen et al., 2022) to generate frame-level embeddings from 8 kHz audio data (Verma et al., 2023). For each 50ms frame, WavLM predicts character probabilities, which are decoded using a beam search algorithm to produce the transcript. To enhance transcription accuracy, we integrate KenLM (Heafield, 2011), a statistical language model that effectively handles the linguistic diversity of Indian code-mixed speech. The transcription process begins with a reduced character set based on Devanagari, which facilitates phonetic alignment and reduces transcription errors. Subsequently, this text is converted to the native language, where spoken words are mapped to their respective languages. Finally, the text undergoes a romanization process to ensure consistency and maintain the pronunciation of English words, enabling seamless handling of multilingual utterances (Verma et al., 2023).

B.2 Speaker Diarization
We adopt a tailored approach for speaker diarization, addressing both dual-channel and mono-channel audio scenarios. In dual-channel diarization, each speaker’s voice is recorded on a separate channel, and timestamps are assigned to speakers, prioritizing the high-energy speaker in overlapping segments. For mono-channel audio, we employ a clustering-based method using Titanet (Koluguri et al., 2021) to generate embeddings for fixed-length audio windows. By comparing these embeddings with the agent’s pre-existing voiceprint, we accurately attribute speech segments to either the agent or the customer.

C Annotation details

C.1 Annotation Software
We used the Inception software (Klie et al., 2018) to annotate CoMuMDR. The software provided the annotators a platform to select the text spans corresponding to an EDU, establish a link between two EDUs, and annotate a relation label for the link. The platform also displayed the description of each annotation label during annotation to remind them of its definition.

C.2 Annotator Profiles and Payment
The annotators were hired as freelance employees to annotate 20 batches of data for a fixed payment of $ 1,179.13. Each batch consists of 50 dialogues and consumes 5 hours per annotator. Hence, the annotators were paid $ 11.79 per hour or $ 0.60 per dialogue.
The annotators had previous experience annotating conversation data for various domains, including the domains covered in CoMuMDR. They are proficient in reading, speaking, and listening to English and Hindi and use both languages in a code-mixed style in everyday communication.

C.3 Annotation Instructions
The annotators were given the following instructions to annotate their batch:
• Dialogue Overview
  – Each dialogue consists of approximately 10 utterances.
  – An utterance is a sequence of phrases, with each phrase separated by punctuation marks.
• Span Identification
  – A span may consist of an entire utterance or one or more phrases within an utterance.
  – Carefully identify spans where a relation might be possible with another span in the dialogue.
• Relation Creation
  – Once relevant spans are identified, create a relational edge between these spans.
  – Select the appropriate label from the defined relation types to describe the connection.
• Edge Constraints
  – No back edges should be created, meaning edges should only flow forward in the dialogue.
• Special Instructions on Acknowledgment vs. Question-Answer Pair
  – Acknowledgment is used for statements that function as conversation continuators, indicating understanding.
  – If an utterance is framed as a question, even if the reply is a simple continuator (e.g., “hmm,” “okay,” “I see”), the relation should be labelled as Question-Answer Pair rather than Acknowledgment.
• By following these steps, you will ensure consistent and accurate annotations across the dialogues. Read the entire dialogue first, identify
Algorithm 1 Span exact match

Require: List of spans \( A, B \)
1: procedure COUNTEXACTMATCHES(A, B)
2:   ExactCount \( \leftarrow 0 \)
3:   for \( a \in A \) do
4:     if \( a \in B \) then
5:       ExactCount \( \leftarrow \) ExactCount + 1
6:     end if
7:   end for
8:   return ExactCount
9: end procedure

Algorithm 2 Span partial match

Require: List of spans \( A, B \)
1: procedure COUNTPARTIALMATCHES(A, B, threshold)
2:   PartialCount \( \leftarrow 0 \)
3:   for \( a \in A \) do
4:     BestMatchScore \( \leftarrow \max_{b \in B} \) Jaccard\((a, b)\)
5:     if BestMatchScore \( \geq \) threshold then
6:       PartialCount \( \leftarrow \) PartialCount + 1
7:     end if
8:   end for
9:   return PartialCount
10: end procedure

potential relations, mark the spans, and then apply the relevant relation edge labels.

The annotators were also given the list of relation labels, their definitions, and appropriate examples as listed in Table 6.

C.4 Annotation Process

A two-party dialogue consists of a list of utterances spoken by two speakers. An utterance is a continuous set of words spoken by a speaker, which may include multiple sentences. The annotators identified elementary discourse units (EDUs) from the utterances for discourse linking and relation labeling. We used clauses as the EDUs based on the definition in Segmented Discourse Relation Theory (SDRT) (Asher and Lascarides, 2005).

Three annotators were recruited to annotate the whole dataset. We divided the dataset into three batches. For each batch, two annotators independently annotated the data, and the third annotator resolved discrepancies. The three batches were rotated in such a way that each annotator annotated two batches and resolved discrepancies on another batch.

C.5 Inter Annotator Agreement Metrics

Table 2 highlights the inter-annotator metrics that we define in Algorithms 1 and 2. We did not rely on off-the-shelf models and algorithms to segment the text into EDUs because of the nature of CoMuMDR. It consists of overlapping utterances and imperfect diarizations, which caused segmentation models to split a potentially single EDU into two parts. The annotators were tasked to select the EDU span, build links between EDUs, and classify relation labels. Thus, we calculated the Kappa inter-annotator agreement based on the overlap between the selected spans of each annotator and the links and relation types between EDUs.

D Model Training Details

We used the same hyperparameter settings as mentioned in the model papers. All the experiments were carried out on an Nvidia 3090 GPU. We mentioned the relevant hyperparameters in Table 7.

E Evaluation Metric

We compute link prediction as a binary classification task between two EDUs. If a link is present in the gold annotations and prediction, it is a True positive link. Similarly, if a link is predicted between two EDUs but is not in the gold annotation, it is a False positive link. Using these definitions, we construct the confusion matrix and calculate the F1-score for link prediction.

A relation \( r_{ji} \) between two EDUs (\( u_j, u_i \)) is classified only if the model predicts a link between (\( u_j, u_i \)). Hence, we first find all the intersecting links between the gold annotated data and predicted links, i.e., \( \forall j, i \) if there is a link \( u_j \) and \( u_i \) in gold and predicted data then capture the gold and predicted relations (\( r_{ji}, r'_{ji} \)). We calculate the link + relation F1-score by using the pairs of gold and predicted relations.

F GPT-4 Template

We experimented with using GPT-4 for discourse parsing on STAC, Molweni, and CoMuMDR. We used the prompt template mentioned in Figure 6.

G Distance between linked EDUs

Fig. 7 (and Table 8) shows the distribution of relative distance between linked EDU pairs for each relationship type. The distance between EDU pairs is defined as the difference between the utterance turn of the head and tail of a link. For example, a link to the same utterance has 0 distance while a link to the next utterance has 1 distance. We merged the statistics of the merged relations (mentioned in Table 6
Figure 4: Distribution of the discourse relation labels for STAC and Molweni datasets. In this plot, we have combined the labels based on our labeling strategy mentioned in Table 6.

for STAC and Molweni. We observe a significant distribution overlap between STAC and Molweni datasets for Correction, Question Extension, Acknowledgment and Question_answer_pair, suggesting their relative similarity. However, for Conditional, Continuation and Contrast there is a difference in the distributions. We also plot the same for CoMuMDR. We notice a significant difference in the distributions; notably that most of the relations have a distance of 1. We also look at the mean and standard-deviation of the relation distances in Table 8. The median distance between linked EDUs for all relations is 1 in all the datasets.

Figure 5: A sample conversation taken from CoMuMDR. Utterances from the customer are marked in purple, and those of the customer center representative are green. The gold and predicted relations are marked on the left and right sides.

You are given a dialogue conversation between an agent and a customer. You have to do the link and relation prediction using SDRT format. You will be given the relations and you have to strictly use those relations only to do the prediction. You will be given the nodes as well in the form of extracted text spans. During link prediction, you have to identify between which nodes there exists a link and what would be the relation.

you have to return the answer in the SDRT format like json. Do not return any extra text or explanation.

Dialogue:
{dia}

Spans:
{spans}

relations:
{rels}

Following is just an example of annotation:
{examples}

Note: For all the instances where a sentence spoken by the same person is broken down into multiple lines, then use dia-continuation relation.

Figure 6: Prompt template used for evaluating GPT-4 as a discourse parser.
Figure 7: Distance between linked EDUs for different corpora
<table>
  <tr>
    <th>Discourse Label</th>
    <th>Description</th>
    <th>Example</th>
  </tr>
  <tr>
    <td>Acknowledgment</td>
    <td>The tail clause is an agreement or disagreement to the head clause</td>
    <td>जी नाम confirm करने के लिए धन्यवाद (Ok, thank you for confirming your name)</td>
  </tr>
  <tr>
    <td>Question-Answer Pair</td>
    <td>The tail is an answer clause to the question in the head clause</td>
    <td>मैं आपकी किस प्रकार सहायता कर सकता हूँ → यह मेरा return के regarding call है<br><i>How can I help you? → This is a call regarding my return</i></td>
  </tr>
  <tr>
    <td>Question-Answer Complaint Pair</td>
    <td>Similar to the Question-Answer Pair, however, the head clause is a customer complaint question</td>
    <td>Sixth मुद्दा last time दिखा रहा था लेकिन अब ninth दिखा रहा → हाँ सर, मैंने high priority issue raise कर दिया है<br><i>(It was showing me on the sixth, now it is showing ninth → Yes sir, I have raised a high priority issue)</i></td>
  </tr>
  <tr>
    <td>Background</td>
    <td>The tail provides supplementary context or information to the subject or object in the head clause. The subject or object in the head clause is the main topic of discussion in the dialogue</td>
    <td>इस विषय में आपने already issue highlight किया है → 29th October की date में ही issue highlight हुआ है, तोहाँ system में show ही रहा है<br><i>(You have highlighted an issue regarding this → The systems shows a issue highlighted on 29th October)</i></td>
  </tr>
  <tr>
    <td>Contrast</td>
    <td>The tail highlights a difference between the subject, predicate, and object interaction in the head clause</td>
    <td>यह complaint आप कर सकते हैं या मुझे online करनी होगी → आपको करनी पड़ेगी<br><i>(Can you raise the complaint or do I have to do it online? → You’ll have to do it)</i></td>
  </tr>
  <tr>
    <td>Correction</td>
    <td>The tail clause is a correction or refinement of the head clause</td>
    <td>आपके headphone बंद है → नहीं, deliver नहीं हुए<br><i>(Your headphones are broken → No, headphones are not delivered)</i></td>
  </tr>
  <tr>
    <td>Question extension (Clarification Question, Question elaboration)</td>
    <td>The tail and head are question clauses from the same speaker. The tail enquires more details, seeks clarity, or elaborates on the head clause with option choices.</td>
    <td>You are receiving complete wrong item right? → Pickup address will be same?</td>
  </tr>
  <tr>
    <td>Conditional (Alternation, Conditional)</td>
    <td>The tail provides choices for the actions dictated in the head or sets up a situation that affects the head clause.</td>
    <td>“Either we go now, or we wait for tomorrow”<br>“If it rains, we’ll stay inside”</td>
  </tr>
  <tr>
    <td>Continuation (Comment, continuation, elaboration, parallel, result)</td>
    <td>The tail adds a remark, extends or elaborates, clarifies, adds related information, or shows the outcome of a previous action</td>
    <td>सुबह आया था पहल message की वाह निच्छुप के लिए निकल चुका है agent → और फिर मेरे पास कुछ देर बाद second message आया की किसी unavoidable event की वजह से pickup नहीं हो पाया गया<br><i>(I got a message in the morning that the agent has left for receiving the pickup → Then I got a message saying that the pickup cannot be completed due to an unavoidable event)</i></td>
  </tr>
</table>

Table 6: Discourse relation labels and their descriptions. We use a subset of the labels presented in the STAC corpus and add another label, Question answer Complaint Pair to capture a specific case in customer center data. The annotators were given these descriptions and examples during the annotation process. In the first column, we highlight the combined discourse labels for annotating the dataset within parentheses.
<table>
  <tr>
    <th>Model</th>
    <th>Optimizer</th>
    <th>learning-rate</th>
    <th>lr-decay</th>
    <th>epochs</th>
    <th>batch size</th>
  </tr>
  <tr>
    <td>Deep Sequential</td>
    <td>AdamW</td>
    <td>1e-1</td>
    <td>0.98</td>
    <td>50</td>
    <td>4</td>
  </tr>
  <tr>
    <td>Hierarchical</td>
    <td>AdamW</td>
    <td>2e-4</td>
    <td>1.00</td>
    <td>20</td>
    <td>1</td>
  </tr>
  <tr>
    <td>Structure-aware</td>
    <td>SGD</td>
    <td>1e-1</td>
    <td>0.98</td>
    <td>10</td>
    <td>1</td>
  </tr>
  <tr>
    <td>SSP-BERT SCIJE</td>
    <td>Adam</td>
    <td>1e-3</td>
    <td>0.75</td>
    <td>100</td>
    <td>4</td>
  </tr>
  <tr>
    <td>SDDP</td>
    <td>AdamW</td>
    <td>2e-5</td>
    <td>1e-8</td>
    <td>3</td>
    <td>4</td>
  </tr>
</table>

Table 7: Hyperparameter settings used to experiment all the discourse parsing models on STAC, Molweni, and CoMuMDR datasets.

<table>
  <tr>
    <th rowspan="2">Relation</th>
    <th colspan="3">STAC</th>
    <th colspan="3">Molweni</th>
    <th colspan="3">CoMuMDR</th>
  </tr>
  <tr>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
  </tr>
  <tr>
    <td>Continuation</td>
    <td>1.17 ± 1.53</td>
    <td>1.65 ± 1.14</td>
    <td>1.06 ± 0.42</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Question answer</td>
    <td>1.78 ± 1.20</td>
    <td>1.56 ± 1.09</td>
    <td>0.99 ± 0.27</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Acknowledgment</td>
    <td>1.67 ± 1.31</td>
    <td>1.41 ± 0.81</td>
    <td>0.95 ± 0.32</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Background</td>
    <td>0.72 ± 1.14</td>
    <td>1.35 ± 0.66</td>
    <td>1.07 ± 0.39</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Correction</td>
    <td>1.67 ± 1.84</td>
    <td>1.41 ± 0.77</td>
    <td>1.00 ± 0.00</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Question Extension</td>
    <td>1.86 ± 1.86</td>
    <td>1.62 ± 1.12</td>
    <td>1.05 ± 0.48</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Conditional</td>
    <td>0.67 ± 1.35</td>
    <td>1.78 ± 1.33</td>
    <td>1.03 ± 0.33</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Contrast</td>
    <td>0.97 ± 1.15</td>
    <td>1.60 ± 1.05</td>
    <td>0.98 ± 0.19</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Question answer complaint</td>
    <td>-</td>
    <td>-</td>
    <td>1.21 ± 0.54</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>dia-continuation</td>
    <td>-</td>
    <td>-</td>
    <td>0.97 ± 0.26</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 8: Mean and standard deviation of distribution of distance between linked EDUs for all corpus

<table>
  <tr>
    <th></th>
    <th>Ack</th>
    <th>dc</th>
    <th>QAP</th>
    <th>QACP</th>
    <th>QE</th>
    <th>Correction</th>
    <th>Continuation</th>
    <th>Conditional</th>
    <th>Background</th>
    <th>Contrast</th>
  </tr>
  <tr>
    <td>Ack</td>
    <td>59</td>
    <td>28</td>
    <td>3</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>3</td>
    <td>0</td>
    <td>3</td>
    <td>1</td>
  </tr>
  <tr>
    <td>dc</td>
    <td>20</td>
    <td>70</td>
    <td>10</td>
    <td>1</td>
    <td>2</td>
    <td>4</td>
    <td>27</td>
    <td>0</td>
    <td>7</td>
    <td>1</td>
  </tr>
  <tr>
    <td>QAP</td>
    <td>28</td>
    <td>17</td>
    <td>42</td>
    <td>0</td>
    <td>1</td>
    <td>6</td>
    <td>15</td>
    <td>1</td>
    <td>0</td>
    <td>0</td>
  </tr>
  <tr>
    <td>QACP</td>
    <td>1</td>
    <td>0</td>
    <td>1</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
  </tr>
  <tr>
    <td>QE</td>
    <td>11</td>
    <td>20</td>
    <td>15</td>
    <td>1</td>
    <td>14</td>
    <td>3</td>
    <td>28</td>
    <td>3</td>
    <td>3</td>
    <td>2</td>
  </tr>
  <tr>
    <td>Correction</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>1</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
  </tr>
  <tr>
    <td>Continuation</td>
    <td>9</td>
    <td>20</td>
    <td>1</td>
    <td>0</td>
    <td>4</td>
    <td>2</td>
    <td>26</td>
    <td>1</td>
    <td>4</td>
    <td>3</td>
  </tr>
  <tr>
    <td>Conditional</td>
    <td>0</td>
    <td>2</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>1</td>
    <td>1</td>
    <td>1</td>
    <td>0</td>
  </tr>
  <tr>
    <td>Background</td>
    <td>2</td>
    <td>9</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
    <td>7</td>
    <td>0</td>
    <td>3</td>
    <td>0</td>
  </tr>
  <tr>
    <td>Contrast</td>
    <td>0</td>
    <td>1</td>
    <td>1</td>
    <td>0</td>
    <td>0</td>
    <td>3</td>
    <td>2</td>
    <td>0</td>
    <td>0</td>
    <td>0</td>
  </tr>
</table>

Table 9: Confusion matrix of discourse link+relation classification done by GPT-4o. We have turned some relations into their relevant acronyms for viewing: Ack-Acknowledgment, QACP-Question Answer Complaint Pair, QAP-question answer pair, QE-question extension, and dc-diarization continuation.

Reviewer paper summaries: his paper proposes CoMuMDR, a code-mixed multimodal multi-domain corpus for discourse parsing in conversations. The corpus contains Hindi-English code-mixed data with both audio and transcribed text, and is annotated with nine discourse relations. The authors experiment with various SOTA baseline models, and their poor performance on CoMuMDR highlights the challenges of multi-domain code-mixed data, pointing towards the development of better models for such realistic settings.

This paper describes a dataset related to discourse parsing. The dataset contains audio and associated transcriptions that include both English and Hindi, potentially code-mixed. The dataset is annotated with discourse parse notation adapted from Semantic Discourse Representation Theory. The paper shows the results of various discourse parsing methods on the dataset. The authors note that they are unable to release the audio and transcriptions due to privacy/licensing issues.

Comments after Author Discussion: This is a weird one. My major complaint is that according to the paper, the transcripts will not be released as part of the dataset. It says this very explicitly, twice. Very, very explicitly. And now in discussion the authors say the transcripts will be released. I just don't know if I believe this? And it suggests a carelessness in the paper writing that makes me mistrust the rest of the paper. Therefore I am not changing my score.
Summary of strengths: The corpus is based on customer service center call data which derive from real-world scenarios, ensuring its practical application value for developing concrete systems.

The dataset covers 2 modals including text and audio, and 2 languages including Hindi and English, enabling its usablity in multimodal and multilingual field.

The paper conduct experiments with various SOTA baseline models, and compare them with existing corpora, highlighting the challenges of CoMuMDR and future research directions.

1. The question of discourse-parsing with code-mixed text is important and interesting.

2. From the descriptions, the dataset is thoughtfully constructed and the authors put a lot of work into manually ensuring the correctness of annotations as well as anonymizing the text.
Summary of weaknesses: Compared to STAC and Molweni, due to privacy reasons, CoMuMDR has a smaller data size, which may limit the training and generalization ability of models. You can try to generate customized data with the potential of outstanding LLMs to effectively expand it.

The annotation of CoMuMDR requires being conducted by professional humans instead of mature models, containing a remarkable workload and restricting its extensibility. Maybe you can try a combination of some domain-specific models to reduce the cost of human work.

The dataset is mainly based on customer service center call data with specific styles and features, which may restrict its universality. You may conduct your method on more realistic areas to fill the gaps in the field.

This is a dataset paper without a dataset. From the descriptions, the dataset is well constructed and would potentially be of interest to the community. However, with the ability to actually release the audio and/or transcriptions, the paper has little value.

I feel bad as I suspect the authors intended to release the dataset and likely fought hard to do so within their organization. The paper is very nicely written and I would likely have voted to accept if the dataset was being released.
Comments, suggestions and typos: Maybe some features specific for customer service should be analyzed.

n/a
Overall assessment scores: 2.5

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