Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs

Firoj Alam; Hunzalah Hassan Bhatti; Shammur Absar Chowdhury

REVIEW 5 major objections 6 minor 9 references

Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs

T0 review · 5 major / 6 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read Data scheduling, rather than raw model scale, is what decides how balanced a low-resource Arabic audio LLM becomes across transcription, summarization, dialect, and emotion tasks.

desk verdict Solid controlled study and a genuinely new Arabic speech summarization resource, but the headline 'best balance' claim is undercut by the paper's own Arabic SSUM judge score; worth refereeing once that discrepancy is addressed. read the letter →

arxiv 2601.12494 v3 pith:5OANUJYY submitted 2026-01-18 cs.SD cs.AIcs.CLeess.AS

Hunzalah Hassan Bhatti , Firoj Alam , Shammur Absar Chowdhury This is my paper · ORCID

classification cs.SDcs.AIcs.CLeess.AS

keywords dataschedulingmulti-taskinstructiontuningArabicspeechsummarizationdialectidentificationemotionrecognitioncurriculumlearningdiversebatchsamplinglow-resourceLLM

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to show that when adapting an audio large language model to Arabic—a setting with dialectal variety, code-switching, and scarce task-specific data—the order in which tasks are introduced and the way training batches are assembled are decisive design levers. It compares four scheduling strategies under a fixed compute budget and reports a clear efficiency-robustness trade-off: curriculum-style training favors generative tasks like transcription and summarization, while diversity-oriented batch sampling boosts emotion and dialect recognition but can hurt generative skills. The proposed two-stage recipe—run the task-progressive curriculum first, then switch to aligner-diversity-based sampling—is claimed to give the most reliable balance across all four task families. The paper also builds and releases an Arabic speech-summarization dataset to make such training and evaluation possible. A sympathetic reader would care because the recipe works without extra compute or data, merely by reordering what the model sees.

What carries the argument

The load-bearing mechanism is the training sampler itself, in two variants. TPC is a staged ordering of tasks (acoustic → paralinguistic → reasoning) that keeps a fraction of earlier data to limit forgetting. ADS is a batch constructor: it max-pools the aligner's hidden states, clusters them into a K=500 codebook, then fills each batch by task proportion, upsamples minority labels, and round-robins across clusters to diversify speakers and acoustic conditions. The aligner—the linear projection carrying speech-encoder features into the LLM space—is the representation that ADS uses to define diversity. TPC→ADS is a schedule that runs the first strategy for part of training, then the second.

What would settle it

Collect a held-out set of naturally recorded Arabic speech (for instance broadcast or interview segments) with human-written summaries. Run the TPC, ADS, and TPC→ADS models on it. If TPC→ADS no longer matches or beats TPC on summarization quality, the paper's central balance claim fails for real-world Arabic audio.

Watch

Extended reading notes

Core claim

The paper's discovery is that, with the same total number of training steps, the scheduling of tasks and batches changes which capabilities an audio LLM develops. Task-Progressive Curriculum (TPC) starts with speech recognition and layers in higher-level tasks, yielding strong ASR and summarization, but leaves paralinguistic tasks under-trained. Aligner-Based Diverse Sampling (ADS) builds batches that respect task priors, balance labels, and cover acoustic diversity via clustering of aligner representations; it speeds early convergence and lifts emotion and dialect scores, but hurts generative stability when used alone. The two-stage TPC→ADS schedule stabilizes the audio-to-text mapping firs

Load-bearing premise

The central claim rests on treating AraMega-SSum, built by translating English news summaries and re-synthesizing the audio with cloned voices, as a valid test of real Arabic speech summarization; if synthetic TTS audio does not behave like natural spontaneous Arabic, the summarization comparisons in the balance argument lose their real-world force.

Editorial extensions

If this is right

With compute fixed, switching from curriculum to diversity-based sampling partway through training lifts dialect and emotion recognition while keeping ASR and summarization close to their curriculum-only levels.
Running diversity-based sampling alone throughout training speeds early convergence but tends to hurt generative tasks such as speech summarization and ASR, so it should not be used as a standalone schedule for balanced multi-task audio tuning.
Task order matters: introducing paralinguistic tasks late can cause negative transfer unless followed by batches that explicitly cover minority labels and acoustic conditions.
For a dominant, well-represented task such as ASR, uniform mixing is sufficient; scheduling choices mainly change outcomes on imbalanced and low-resource tasks.
The released Arabic speech summarization dataset makes end-to-end Arabic audio summarization trainable and benchmarkable for the first time.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the benefit of the two-stage schedule comes from mastering canonical patterns before seeing exceptions, the same recipe is a natural candidate for other dialect-rich, low-resource languages where ASR data is plentiful but paralinguistic resources are scarce.
The switch point between TPC and ADS is an unexplored hyperparameter; tuning it could reveal a trade-off curve rather than a single universal schedule, and different task mixes may want different switch points.
Because the Arabic speech-summarization benchmark is built by translating English news summaries and re-synthesizing speech with cloned voices, its test set may not reflect spontaneous natural Arabic; validating the summarization results on naturally recorded audio would be a direct stress test.
The aligner-embedding diversity criterion could be reused beyond audio, wherever a shared latent representation sits between an input encoder and a language decoder and labels are imbalanced.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: the paper does something real — it builds the first Arabic speech summarization dataset, AraMega-SSum (50k pairs), and runs a controlled comparison of four scheduling strategies on one backbone with identical step counts. That controlled setup is genuinely useful; the field mostly has multi-task recipes without this level of isolation. The ADS mechanism (K-means on aligner embeddings to diversify batches) is a reasonable contribution, though it combines known ingredients.

Credit where due: the design separates modality alignment from scheduling, freezes encoder/aligner in Phase 2, keeps compute fixed, and evaluates on a broad suite (ASR, DID, SER, summarization). The finding that TPC gives strong generative performance while ADS improves paralinguistic tasks is coherent, and the efficiency–robustness framing is fair. If the dataset is released as promised, that alone is a contribution worth citing.

The soft spots are real. The biggest is internal: Table 4 lists TPC+ADS Arabic SSUM judge score 6.73 — tied with the untrained base and 1.10 below UM (7.83) — while Section 5.2 claims "small differences on SSUM (Δjudge < 1.0)" relative to TPC→ADS. Either the table is wrong or the claim averages away a sizable Arabic regression. Since "balanced performance across ASR, summarization, dialect, and emotion" is the paper's central assertion, this needs a fix and an explanation, not a hedge. Second, all Phase 2 numbers are single runs; some headline gaps are noise-level (DID 87.17 vs 87.12; MGB2 12.49 vs 12.61). Third, AraMega-SSum is synthetic TTS audio, so SSUM results are only as good as the transfer assumption to natural Arabic; the authors acknowledge this in part and do evaluate other tasks on real speech, but the summarization claim is load-bearing. Fourth, the Gemini comparison is not apples-to-apples: DID numbers come from an external paper, and Gemini ASR is not run in the same harness. Minor: the translation-quality tables report near-perfect scores from GPT-4.1 and human judges with no variance, which reads more like a sanity check than an evaluation.

I don't think the main methodological idea collapses. The internal SSUM judge inconsistency is the thing that makes the "best balance" claim unproven as written. This deserves a serious referee — conditional accept shape, not desk reject. I'd send it out, but I'd expect the revision to resolve the judge discrepancy, add seeds or confidence intervals, and release data/code before publication.

Referee Report

5 major / 6 minor

Summary. The paper presents a controlled comparison of four multi-task instruction-tuning schedules for adapting Qwen2.5-Omni (7B) to Arabic-centric audio tasks: ASR, speech/text summarization, dialect identification, and emotion recognition. A two-phase procedure is used: phase 1 performs large-scale ASR-based language-centric alignment; phase 2 keeps the encoder and aligner frozen and trains only LoRA adapters under uniform mixing (UM), task-progressive curriculum (TPC), aligner-based diverse sampling (ADS), and a TPC-to-ADS hybrid. The authors also introduce AraMega-SSum, a synthetic Arabic speech summarization dataset built by translating English Gigaword-derived speech-summary pairs and re-synthesizing Arabic audio with XTTS-v2 voice cloning. The main claimed finding is an efficiency–robustness trade-off in which ADS improves paralinguistic tasks but harms generative stability, while TPC→ADS provides the best overall balance across tasks.

Significance. If the central claim holds, the paper would provide practically useful guidance for adapting audio LLMs to low-resource, dialect-rich settings while keeping compute fixed, and AraMega-SSum would fill a real gap as a publicly released Arabic speech summarization resource. The controlled same-step comparison across four schedules is a genuine strength, as is the intention to release code, data, and training resources. However, the headline 'best balance' conclusion is not yet supported: the paper's own Arabic SSUM judge score for TPC→ADS is the worst among trained strategies and is contradicted by the text's Δjudge<1.0 claim; all Phase-2 tables are single-run; and the summarization benchmark is entirely synthetic. These issues are fixable but require additional analysis or careful qualification.

major comments (5)

[§5.2 vs. Table 4] The central 'best overall balance' claim is contradicted by the paper's own Arabic SSUM results. In Table 4, TPC→ADS receives a GPT-4.1 judge score of 6.73 on Arabic SSUM, tied with the untrained Base and 1.10 points below UM (7.83) and 1.07 below TPC (7.80). Section 5.2 states that relative to TPC→ADS, UM shows 'SSUM (Δjudge < 1.0)'; this can only be true if Arabic and English gaps are averaged, since the Arabic gap is 1.10. Because 'balance' explicitly includes summarization, the per-language breakdown must be reported and the averaging justified, or the conclusion must be qualified to exclude Arabic SSUM. ROUGE-L and BERTScore do not resolve the issue, as the judge is the metric used to claim quality.
[§3.1, §3.3, Tables 6–7] AraMega-SSum is built from translated English news sentences and XTTS-v2 voice-cloned Arabic speech. The SSUM benchmark is therefore wholly synthetic audio, yet 'balanced performance across ASR, summarization, dialect, and emotion' includes this task. The manuscript presents no evidence that synthetic short news utterances transfer to natural spontaneous Arabic speech. The translation-quality checks in §3.1 are near-ceiling (≥9.9/10 for both LLM and 200-item human evaluation), providing little discriminative quality control, and no human evaluation of the synthesized Arabic audio or of model-generated Arabic summaries is reported. The authors should either provide transfer/robustness evidence or state explicitly that the summarization conclusions apply only to synthetic short-form audio.
[§4, Tables 3–5] All Phase-2 results are reported from single runs with no error bars or repeated seeds. Several headline comparisons are numerically small: DID 87.17 vs. 87.12 for TPC→ADS vs. TPC, MGB2 WER 12.49 vs. 12.61 for TPC vs. UM, and Arabic TSUM ROUGE-L 38.04 vs. 37.14 for TPC→ADS vs. ADS. The claim that TPC→ADS is 'more reliable' than alternatives is not supported for margins of this size without variance information. Reporting 2–3 seeds or otherwise characterizing run-to-run noise is necessary before reliability language is justified.
[Algorithm 1, §4, §5.2] ADS itself depends on several untested choices: the cluster count K=500, the 3% representative subset, the task-prior distribution used to set per-task batch sizes, and the TPC→ADS switch point. The Limitations section concedes that ADS ablations were not run. Since the paper's main message is that scheduling/batch construction is a key design lever, the result may be tied to these specific hyperparameter values rather than to diversity-based sampling in general. At minimum, a sensitivity discussion or a small ablation of K and the switch point is needed to support the generality of the conclusion.
[Table 5, Table 3] The Gemini/SOTA row in Table 5 mixes a Gemini SER result with a DID result taken from Althubaiti et al. (2025), under unknown evaluation conditions. Similarly, Table 3 labels Gemini as an 'upper bound' even though Gemini is worse than the trained models on SADA, ESCWA, DACS, LibriSpeech, and L2-ARCTIC. These externally sourced numbers should be separated, clearly labeled, and excluded from the main ranking; otherwise the abstract's claim of 'outperforming large proprietary models' is not consistently supported by the presented comparisons.

minor comments (6)

[Abstract, §2.3] The abstract lists strategies as '(i), (ii), (iiii)' — should be (iii). Also, §2.3 says TPC trains in 'five sequential stages' but lists only three blocks (ASR → DID/SER → TSUM/SSUM); please clarify.
[Table 2 vs. Table 5] Table 2 specifies DID evaluation as weighted F1 on ADI-17, while Table 5 reports DID as accuracy. The metric should be consistent, and the text should state which is used for the headline DID claims.
[Dataset naming] The MegaSSUM source corpus is referred to inconsistently as 'MegaSSUM', 'Mega-SSum', and 'MegaSUM-SSum'. Please standardize.
[Tables 3–5] The caption 'Best scores are highlighted in blue' is not meaningful in a black-and-white printout; consider bolding or adding symbols.
[Table 4 caption] 'hypothised' should be 'hypothesized'.
[References] Radford et al. (2023a) and (2023b) refer to the same paper; the entry for Rubenstein et al. (2021) contains 'and 1 others' and a malformed title. Please fix the bibliography.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; the scheduling comparison is empirical and self-contained, with only a minor non-load-bearing self-citation (MenaSpeechBank).

full rationale

The central claim — that TPC→ADS provides the best overall balance across ASR, summarization, dialect, and emotion — is an empirical result from a controlled comparison of four training strategies under identical compute (Section 2.2: 'All scheduling strategies in this phase use the same compute budget, meaning the same total number of training steps'). No equation defines a predicted quantity as its own input; no fitted parameter is renamed as a prediction. ADS clustering on 3% of aligner representations is a batch-construction mechanism, not a fitted target, and the relative strategy ordering in Tables 3–5 is not forced by the dataset-generation pipeline. The only self-citation is 'We use reference speaker audio from MenaSpeechBank (Ali et al., 2026)' (Section 3.1); that work shares authors with the present paper, but it is used only to source speaker embeddings for TTS voice cloning and does not determine the measured outcomes or the scheduling conclusions. The apparent tension flagged by the skeptic — Arabic SSUM judge score 6.73 for TPC+ADS versus 7.83 for UM while Section 5.2 claims Δjudge < 1.0 — is a correctness/consistency concern, not circularity, because even if that number were wrong the claim would not reduce to its inputs by construction. Score 2 reflects the minor self-citation; the derivation itself is self-contained and independent.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim relies mainly on scheduling hyperparameters and on the validity of the synthetic benchmark and LLM-judge evaluation. No new physical or architectural entities are introduced. The largest hidden costs are the XTTS-v2 synthetic-speech assumption and the comparability of externally cited baselines.

free parameters (5)

ADS cluster count K = 500
Chosen by hand for the K-means codebook on aligner embeddings; directly controls the acoustic-diversity constraint in batch construction (Sec. 2.3).
ADS representative subset size = 3% (~75 hours)
The clustering is run on a 3% subset to reduce compute; the cluster structure and hence batch diversity depend on this choice (Sec. 4).
Task prior distribution for ADS batches = Natural dataset distribution
ADS preserves original task proportions in each batch; this choice determines how much ASR dominates and is load-bearing for the claimed balance (Sec. 2.3, Algorithm 1).
TPC stage order = ASR → DID, SER → TSUM, SSUM
The curriculum ordering is a free design choice; all TPC and TPC→ADS results depend on this exact order (Sec. 2.3B).
TPC→ADS switch point = Not specified precisely
The hybrid runs TPC for an 'initial portion' then ADS, but the exact step fraction is not reported; the balance result depends on this switch point (Sec. 2.3D).

assumptions (4)

domain assumption Speech synthesized by XTTS-v2 voice cloning is a valid proxy for real Arabic speech in summarization training and evaluation.
Sec. 3.1 constructs AraMega-SSum with TTS and voice cloning; Sec. 3.3 uses its test set to report SSUM numbers. If synthetic audio does not match natural spontaneous Arabic, the summarization benchmark is not a real-speech benchmark.
domain assumption GPT-4.1 LLM-as-a-judge scores (and the human subset) are reliable quality measures for Arabic summary and translation quality.
Appendix A reports near-ceiling scores (9.93-10.0) with no error analysis or disagreement metrics; the summary evaluation in Table 4 relies on this judge.
domain assumption Aligner embeddings capture acoustic-semantic diversity relevant to downstream tasks, so K-means clusters in that space are meaningful for batch construction.
Sec. 2.3C makes this the foundation of ADS; if aligner clusters do not track speaker/acoustic/task-relevant variation, the diversity constraint has no effect.
domain assumption The Gemini/SOTA numbers in Table 5, including DID results from Althubaiti et al. (2025), are comparable to the authors' own evaluation runs.
Used to support the abstract claim that TPC→ADS outperforms Gemini-2.5-Pro on discriminative tasks; without a shared evaluation harness, cross-paper comparison is unreliable.

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Cite this review

Pith. "Pith review of Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs." pith.science (2026). https://pith.science/paper/5OANUJYY

@misc{pith2026260112494,
  author       = {Pith},
  title        = {Pith review of: Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5OANUJYY}},
  note         = {Machine review of arXiv:2601.12494}
}

read the original abstract

Audio large language models (LLMs) enable unified speech understanding and generation, but adapting them to linguistically complex and dialect-rich settings such as Arabic-English remains challenging. We present a controlled study of multi-task instruction tuning for an Arabic-centric audio LLM across generative tasks, including automatic speech recognition (ASR) and speech and text summarization, as well as discriminative tasks, including dialect identification (DID) and speech emotion recognition (SER), in a resource-constrained setting. To support end-to-end Arabic speech summarization, we introduce AraMega-SSum, the first Arabic speech summarization dataset designed for training and benchmarking Arabic-centric audio LLMs. We compare four training strategies: (i) Uniform Mixing (UM), (ii) Task-Progressive Curriculum (TPC), (iii) Aligner-Based Diverse Sampling (ADS) for training-time batch construction, and (iv) a two-stage TPC->ADS strategy. Our results reveal a clear efficiency-robustness trade-off. TPC achieves the strongest performance on generative tasks, including ASR and summarization. ADS improves paralinguistic tasks but reduces generative stability when used alone. The two-stage TPC->ADS strategy provides the best overall balance, achieving the strongest DID and SER performance while outperforming large proprietary models such as Gemini-2.5-Pro on discriminative tasks. We will publicly release AraMega-SSum together with all experimental resources to support future research in Arabic speech understanding.

Figures

Figures reproduced from arXiv: 2601.12494 by the authors.

**Figure 3.** Emotion label distribution across training [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

**Figure 4.** Dialect distribution in the training set. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

Figures from the paper (5 more)

**Figure 2.** Figure 2: Training loss comparison across multi-task [PITH_FULL_IMAGE:figures/full_fig_p013_2.png]

**Figure 5.** Figure 5: Sample speech summarization instances from [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]

**Figure 6.** Figure 6: Prompt used for LLM-as-a-judge summarization evaluation. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]

**Figure 7.** Figure 7: Prompt used for LLM-as-a-judge translation evaluation. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]

**Figure 8.** Figure 8: System and user prompts used for multi-task training. , 16 [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

9 extracted references · 2 linked inside Pith

[1]

Ignore all anonymization tokens in English
[2]

Ignore number differences caused by anonymization
[3]

Ignore name differences and transliteration variations
[4]

Be lenient with phonetic spellings in Arabic
[5]

Focus only on semantic meaning and core information
[6]

"" USER_PROMPT_TEMPLATE =

Judge based on events, facts, actions, relationships, and intent. Score each criterion as an INTEGER from 1 to 10: - semantic_equivalence - information_preservation - contextual_accuracy - completeness - coherence Return ONLY a valid JSON object matching the schema exactly. """ USER_PROMPT_TEMPLATE = """Arabic translation: {arabic_transcription} Original ...
[2015]

In arXiv preprint arXiv:1510.08484

MUSAN: A Multipurpose Corpus for Music and Noise. In arXiv preprint arXiv:1510.08484. Fanar Team, Ummar Abbas, Mohammad Shah- meer Ahmad, Firoj Alam, Enes Altinisik, Ehsannedin Asgari, Y azan Boshmaf, Sabri Boughorbel, Sanjay Chawla, Shammur Chowd- hury, Fahim Dalvi, Kareem Darwish, Nadir Durrani, Mohamed Elfeky, Ahmed Elma- garmid, Mohamed Eltabakh, Maso...

arXiv 2025
[2023]

"" USER_PROMPT_TEMPLATE =

SpeechGPT: Empowering large lan- guage models with intrinsic cross-modal con- versational abilities. In Findings of the Associ- ation for Computational Linguistics: EMNLP 2023, pages 15757--15773, Singapore. Associa- tion for Computational Linguistics. Tianyi Zhang, V arsha Kishore, Felix Wu, Kilian Q. Weinberger, and Y oav Artzi. 2020. Bertscore: Evaluat...

2023

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[2024]

ISCA. Guoguo Chen, Shuzhou Chai, Guan-Bo Wang, Ji- ayu Du, Wei-Qiang Zhang, Chao Weng, Dan Su, Daniel Povey, Jan Trmal, Junbo Zhang, Mingjie Jin, Sanjeev Khudanpur, Shinji Watan- abe, Shuaijiang Zhao, Wei Zou, Xiangang Li, Xuchen Y ao, Y ongqing Wang, Zhao Y ou, and Zhiyong Y ...

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Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.

[1] [1]

Ignore all anonymization tokens in English

[2] [2]

Ignore number differences caused by anonymization

[3] [3]

Ignore name differences and transliteration variations

[4] [4]

Be lenient with phonetic spellings in Arabic

[5] [5]

Focus only on semantic meaning and core information

[6] [6]

"" USER_PROMPT_TEMPLATE =

Judge based on events, facts, actions, relationships, and intent. Score each criterion as an INTEGER from 1 to 10: - semantic_equivalence - information_preservation - contextual_accuracy - completeness - coherence Return ONLY a valid JSON object matching the schema exactly. """ USER_PROMPT_TEMPLATE = """Arabic translation: {arabic_transcription} Original ...

[7] [2015]

In arXiv preprint arXiv:1510.08484

MUSAN: A Multipurpose Corpus for Music and Noise. In arXiv preprint arXiv:1510.08484. Fanar Team, Ummar Abbas, Mohammad Shah- meer Ahmad, Firoj Alam, Enes Altinisik, Ehsannedin Asgari, Y azan Boshmaf, Sabri Boughorbel, Sanjay Chawla, Shammur Chowd- hury, Fahim Dalvi, Kareem Darwish, Nadir Durrani, Mohamed Elfeky, Ahmed Elma- garmid, Mohamed Eltabakh, Maso...

arXiv 2025

[8] [2023]

"" USER_PROMPT_TEMPLATE =

SpeechGPT: Empowering large lan- guage models with intrinsic cross-modal con- versational abilities. In Findings of the Associ- ation for Computational Linguistics: EMNLP 2023, pages 15757--15773, Singapore. Associa- tion for Computational Linguistics. Tianyi Zhang, V arsha Kishore, Felix Wu, Kilian Q. Weinberger, and Y oav Artzi. 2020. Bertscore: Evaluat...

2023