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AUTHORS RESPONSES
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1. [FIXED] It would be good to proofread it for the camera-ready submission as it currently contains grammatical errors and the writing is not always fluent.  The use of some terminology can also be improved: for instance, instead of 
-- [FIXED] "instructive large language model (LLM) training" ->  "LLM instruction-tuning" 
-- [FIXED] "instructive language models" -> "instruction-tuned language models"
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2. [FIXED] Clarity of writing can also be improved: for example,
- It is not entirely clear what Table 1 covers and the explanation of its content (and the importance of the included statistics) should be explained in the main text.
2.1 Please make sure that all abbreviations and terms (mathematical and other) are first defined in text.
2.2 [NOTED] It is unclear why a specific model (Flan-T5) was used as this is never explained in the paper.
2.3 [NOTED, need to FIX] It is unclear whether the idea outlined in the "Spans Correction" subsection of Section 3 was included in the submission or whether it is a suggestion for future improvements. How do the results with this correction compare to the results without such a correction?
2.4 [FIXED TABLE 1 (SPLITTED)] Table 1 & 4 seems very cramped and it's hard to interpret it in one go. Could these items be re-formatted?
2.5 [FIXED] Figure 3 has the legend occluding the line plot. Could these items be re-formatted?
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3. [NOTED] The paper currently misses a dedicated Previous Work section and does not explain in enough detail the methodology of Hao et al. (2023), which was used as the basis for the proposed framework. Without such a dedicated discussion it is hard to put the current work in context, make meaningful comparisons, and judge whether the proposed solution is fully based on the previous work or is innovative.
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4. [FIXED] Subsection 1.2 contains references to Section 1, i.e., essentially to itself. If there is a specific bit that you try to refer to, introduce links to subsections.
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5. [FIXED] Make sure all text is in the same font â€“ e.g., prompt templates in Section 2 are written in a smaller font for no obvious reason.
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8. [OK] Originality / Innovativeness - In regards to the literature in emotion cause analysis, very few have attempted Chain of Thought framework in this area.
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9. Soundness / Correctness		
9.1. [RESPONSE: we did not provide the related ablation studies] THoR engine chains probabilities thrice. Perhaps using other alternatives of CoT which use less 'hops' could prove beneficial?
9.3. [RESPONSE: Limitation of the work caused by task definition] 'Future' contexts are ignored which may be ahead in future time step, but are crucial for this task, as the conversations could be simultaneous. 
9.2. [RESPONSE: In the final algorithm we rely on string.punctuation only in the case of checking criteria satisfaction] The dataset has some special characters from string.punctuation which are present in the label spans. Were they incorporated appropriately in the span correction algorithm?
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10. [RESPONSE: Due to the 5 + 1 pages limit, we can't provide extra review and comparison] Meaningful Comparison - 1. No prior work has been shown related to efficacy of prior approached or CoT-based frameworks.
11. [FIXED] The idea sounds great but efficacy of THoR cause and THoR cause-RR is questionable when compared to finetuend Flan-T5 base for D cause. Figure 3 shows that in later epochs (> 5) prompt based method performs better than THoR cause and THoR cause-RR.
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13. [RESPONSE: Algorithm-based spans correction technique relies on string.punctuation] Is the span correction algorithm impacted by the choice of punctuation set? If so, string.puntuation should be avoided as it contains characters which are present in the dataset which are valid prefixes.
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14. [FIXED] Figure 3 might be showing that in later epochs (> 5) prompt based method performs better than THoR cause and THoR cause-RR. on dev set. Is this true? Does the metric rebounds and increases?

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CAMERA-READY IMPROVEMENTS:
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1. FIXED in stat tables: future -> past (since we do not extract relations that goes from "future" to the past)
2. Increased font size.
3. No more word "instructive"
4. Clarified Flan-T5 usage (see Setup. paragraph)
5. Methodology starts once the task is defined
6. Datasets and Experiments: removed mentioned restriction on utilized resources (authors allow to use only publicly provided competition data).
7. Removed pre-trained since it might be mistreated as pre-training from scratch. We use instead instruction-tuning /tuning, prefixed with "preliminary".
8. removed "engine" entries completely.
9. Figures are not does not overlap fine-tuning information.
10. Unfoturnately, we do not have available results for Flan-T5-base tuned with cause on official evaluation.

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NOTES:
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Impact of Ideas or Results - Incorporating CoT framework has not been done extensively in Emotion Cause Analysis (ECA), but this might be cited as a way to benchmark these frameworks for ECA.

6.[SOLVED: LIMITATION, WON'T FIX] Appropriateness - The paper addresses a way to get causal utterances, emotion labels for a given utterance. But, it doesn't yield a way to extract casual spans. 