{"id":"03d0aaa8-b18b-491b-933c-a533535d6aed","arxiv_id":"2505.06599","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":3,"one_line_summary":"A Persian G2P system using an LLM-generated \"Pinglish\" intermediate representation and a seq2seq model reports PER 0.0196 and BLEU 94.6, but the supporting tables are missing and the evaluation is largely self-referential.","lead":"The paper builds a Persian text-to-speech pronunciation system that first turns Persian into a simplified Latin-based \"Pinglish\" with one symbol per sound, then trains a sequence-to-sequence model to produce that representation. It claims better accuracy than prior Persian G2P tools, but the evidence tables are empty and the test data comes from the same LLM pipeline that made the training data.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The reported PER of 0.0196 is computed against GPT-4o-generated Pinglish labels with no human validation, so it measures fidelity to the LLM generator rather than Persian pronunciation accuracy.","rationale":"The reader's REJECT verdict is based on missing evidence and circular evaluation. The stress-test pass identifies the same load-bearing assumption as the reader: the GPT-4o-generated Pinglish labels must be phonemically correct for the reported PER to mean anything. This assumption is the weakest link in the central claim because every reported quantity (BLEU 94.6, PER 0.0196) is computed against these unvalidated labels. If the labels contain systematic errors, the model could achieve arbitrarily low PER by memorizing those errors, and the claimed superiority over existing tools would be an artifact of the evaluation. The paper offers no independent support: no human evaluation, no released dataset or code, and no completed baseline table. The proposed concrete test directly settles whether the labels are accurate by recomputing PER against expert human transcriptions. Since this concern reinforces, rather than changes, the reader's REJECT verdict, the verdict remains UNCHANGED.","tokens_in":11897,"tokens_out":3439,"duration_ms":35763,"concrete_test":"Take a random sample of 500 sentences from the test split (or from SentenceBench) and have two native Persian linguists independently produce phonemic transcriptions (IPA or a verified version of Pinglish), resolving disagreements. Compute the trained model's PER against these human references, using the same alignment as Equation (2). If the PER is substantially higher than 0.0196 (e.g., above 5%), the reported number reflects fidelity to the LLM generator rather than pronunciation accuracy. Additionally, report the completed Table 4 with the same human-reference metric on SentenceBench; if those numbers are missing or much worse, the state-of-the-art claim is unsupported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central empirical claim is that the proposed intermediate-language model achieves a phoneme error rate of 0.0196 and outperforms existing Persian G2P systems. This claim requires that the Pinglish labels used both as training targets and as test references are accurate Persian phonemic transcriptions. The paper provides no such evidence. Section 3.1.2 describes the labels as LLM-generated Romanized text refined by a rule-based post-processor; Section 3.1.3 states the dataset is 64,000 entries; Section 4 then trains on 62,000 entries and evaluates on a random 1,000-entry split of the same corpus. No native-speaker or expert validation of the labels is reported anywhere in the manuscript. Consequently, the reported PER of 0.0196 is a measure of how well the model reproduces GPT-4o's Pinglish outputs, not a measure of how accurately it pronounces Persian. Unless the Pinglish labels themselves are shown to be phonemically correct, the headline numbers cannot support the 'new benchmark' conclusion. The promised comparison on SentenceBench (Table 4) is an empty placeholder, so no external validation is actually presented.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes an intermediate language called \"Pinglish\" for Persian grapheme-to-phoneme (G2P) conversion. The pipeline consists of GPT-4o-generated Romanized Persian labels refined by rule-based post-processing, a custom BPE tokenizer, and an EncoderDecoder transliteration model trained on 62,000 entries (augmented to 195,000). The authors report a BLEU score of 94.6 and a phoneme error rate (PER) of 0.0196, and claim that this establishes a new benchmark for Persian G2P, with particular strengths in Ezafe detection and homograph disambiguation. The experimental results section, however, contains only empty placeholders for Tables 2, 3, and 4, and the evaluation is conducted against a random split of the same LLM-generated corpus, with no human validation and no reported comparison against existing systems.","tokens_in":12163,"tokens_out":2641,"duration_ms":27383,"significance":"If the reported results were supported by a verified reference standard and a fair baseline comparison, the intermediate-language approach would be a useful contribution for low-resource G2P conversion, especially for handling Ezafe and homographs. The authors provide some concrete architectural details, a reasonably sized dataset, and a clear motivation for avoiding IPA in favor of a compact one-to-one phoneme-to-character representation. However, the central empirical claims are currently unsupported: the three main result tables are empty, the reference labels are generated by the same LLM used to create training data, and no named baseline is evaluated on the same test set. The work therefore cannot presently substantiate the stated \"new benchmark\" conclusion.","major_comments":[{"comment":"Tables 2, 3, and 4, which are supposed to report Ezafe detection accuracy, homograph disambiguation results, BLEU/PER, and the SentenceBench comparison, are empty placeholders in the manuscript. Since the conclusion's central claim of achieving BLEU 94.6 and PER 0.0196 rests on these results, and since the text explicitly states that Table 4 shows superiority over an alternative approach, the absence of all numerical data makes the headline results unverifiable.","section":"Section 4, Tables 2-4"},{"comment":"The PER of 0.0196 is computed against GPT-4o-generated Pinglish labels, with no native-speaker or expert validation reported. Because the model is trained on a random split of the same LLM-generated corpus and evaluated on another split of that corpus, the reported error rate measures how well the model reproduces the generator's transliteration behavior rather than how accurately it pronounces Persian. The manuscript provides no evidence that the Pinglish labels constitute a correct phonemic reference, so the central benchmark claim is not established.","section":"Sections 3.1.2, 3.1.3, and 4"},{"comment":"The abstract states that the homograph lexical database is constructed \"utilizing formal concept analysis\" (FCA), yet no FCA component appears anywhere in the method described in Section 3. The dataset construction pipeline (Sections 3.1.1-3.1.3) uses LLM prompting and rule-based post-processing only. This discrepancy means a claimed contribution is never actually implemented or evaluated.","section":"Abstract and Section 3"},{"comment":"No baseline system is evaluated on the authors' test set. The related work mentions PersianG2P, persian_phonemizer, and Rabiee's Persian_g2p with a PER of 3.9%, but the experimental section reports no comparison against any of these systems on a common benchmark. The only proposed external evaluation, the SentenceBench comparison, is an empty table, so the claim of \"superior performance compared to existing state-of-the-art approaches\" is unsupported.","section":"Section 5, Table 4"},{"comment":"The augmentation procedure merges sentences and splits them at non-Ezafe positions to expand the training set from 64,000 to 195,000 samples. The paper asserts that this preserves \"linguistic integrity,\" but no analysis or experiment validates that the augmented examples preserve valid phonemic context. Since the augmented data are used to train the final model, this assumption is load-bearing for the reported performance and needs direct verification.","section":"Section 4, data augmentation"}],"minor_comments":[{"comment":"The tokenization description is ambiguous: it says \"maximum token length of less than 4\" and later sets \"maximum subword length is limited to 3,\" but the relationship between these statements and the BPE hyperparameters (vocabulary 2372, minimum frequency 100) is not explained.","section":"Section 3.3"},{"comment":"Figures 1 through 4 are referenced in the text as schematic overviews of the methodology, dataset construction, mapping examples, and architecture, but the manuscript does not contain the actual figure images, only captions. This makes the methodological description difficult to follow.","section":"Figures 1-4"},{"comment":"The B-Plus podcast data are mentioned as a source of informal Persian, but the paper never states how many sentences were obtained, how they were preprocessed, or how they were combined with the LLM-generated formal and informal data. The dataset description in Section 3.1.3 mentions only the 64,000 generated entries.","section":"Section 3.1.1"},{"comment":"The writing contains frequent grammatical and formatting issues, such as \"conversion(G2P)\" with no space, \"grapheme-to-phoneme conversion with homographs with multiple pronunciations disambiguation\" in the title, and the sentence \"also the findings of this research are generalizable\" in Section 6. A thorough editorial pass is needed.","section":"Throughout"},{"comment":"The references to prior work are often imprecise: for example, Kaamel-Dict and Sentence-Bench are attributed to reference [1] in one place and to [2] and [3] in the references list, and the relation between [1] and [2]/[3] is not clearly stated.","section":"Section 2"}],"recommendation":"reject","confidential_remarks":"The manuscript is not ready for publication in its current form. The central empirical claims are unverifiable because the main result tables are empty, the evaluation reference is generated by the same LLM used to create training data, and no baseline comparison is provided. These issues cannot be fixed by local revisions; they require new experiments with a validated reference standard and a proper comparison. I would encourage the authors to resubmit after performing those experiments and substantially revising the manuscript."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this paper has a reasonable core idea — a one-to-one Latin intermediate representation ('Pinglish') for Persian G2P, with a BPE tokenizer and a small encoder-decoder trained to transliterate — but it is not in a reviewable state. The headline numbers (BLEU 94.6, PER 0.0196) sit on empty evidence.\n\nWhat is genuinely new: the Pinglish intermediate language, with a defined phoneme-to-character mapping and excluded ambiguous letters, is a legitimate engineering choice. Training a seq2seq model on LLM-generated Romanized Persian is a plausible low-cost strategy, and the paper describes the architecture and tokenization details clearly enough to reproduce if the data were released. The related work on Persian G2P tools is real and relevant.\n\nWhere it falls apart: Tables 2, 3, and 4 are empty placeholders. There is no baseline comparison, no variance, no significance test, and no error analysis. The only reported numbers appear inline in Section 4. The test set is a 1,000-entry random split of the same 64,000-entry corpus that produced the training labels. Those labels were generated by GPT-4o and post-processed by rules; no native-speaker or expert validation is reported. So the PER measures how well the model reproduces GPT-4o's transliterations, not how accurately it pronounces Persian. That is the load-bearing weakness.\n\nAlso, the abstract claims a formal concept analysis component for homograph disambiguation. The methodology contains no trace of FCA — no equations, no algorithm, no mention outside the abstract. Either the section is missing or the claim is unsupported. Either way, the paper as written does not deliver what it promises.\n\nThe B-Plus podcast data is mentioned as collected, but the dataset section describes only the 64,000 Pinglish entries; it is unclear how the podcast subtitles contributed. Minor compared to the empty tables.\n\nWho is this for? Someone working on low-resource G2P might find the Pinglish idea worth considering, but only as a proposal. The empirical claims are not usable.\n\nRecommendation: desk reject in current form, with an invitation to resubmit when Tables 2-4 actually contain numbers, the test set is externally validated (or at least human-checked), baselines such as Rabiee's Persian_g2p are run on the same data, and the FCA claim is either implemented or removed. It is not ready for referee time yet.","headline":"A sensible intermediate-language idea drowned by missing results: the four result tables are empty, the promised formal concept analysis is absent, and the single PER number is measured against LLM-generated labels.","tokens_in":12671,"tokens_out":2444,"would_cite":false,"duration_ms":23844,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that converting Persian text to a compact Romanized intermediate language, then transliterating with a sequence-to-sequence model, reaches Persian grapheme-to-phoneme accuracy of 1.96 percent phoneme error and BLEU 94.6.","keywords":["grapheme-to-phoneme conversion","Persian","intermediate representation","Pinglish","homograph disambiguation","Ezafe","sequence-to-sequence transliteration","polyphones"],"falsifier":"Take a held-out set of Persian sentences that includes known homographs and Ezafe contexts, transcribe them into phonemes by hand or from an independent pronunciation dictionary, and compare the model's Pinglish output against those human transcriptions; if the phoneme error rate against human labels is materially higher than 0.0196, the reported accuracy is measuring agreement with the generator rather than pronunciation accuracy.","tokens_in":11728,"feed_emoji":"🗣️","tokens_out":8700,"duration_ms":77949,"temperature":0.7,"pith_summary":"The paper tries to show that Persian grapheme-to-phoneme conversion becomes more accurate and cheaper if the problem is split in two: first turn Persian script into \"Pinglish,\" a deliberately compact Romanized intermediate in which every Persian phoneme has exactly one character, then use a small sequence-to-sequence transliteration model to read Persian and write Pinglish. On this pipeline the authors report a BLEU score of 94.6 and a phoneme error rate of 1.96 percent, and they report strong performance on the two phenomena that trip up prior Persian G2P tools: homographs with multiple pronunciations and the unwritten Ezafe vowel. The system is trained on about 64,000 Persian–Pinglish sentence pairs, expanded to 195,000 by merging and splitting at non-Ezafe boundaries, covering formal and informal Persian. If the numbers are taken as stated, the framework would be a new benchmark for Persian G2P and a template for other homograph-rich languages.","feed_headline":"Pinglish layer cuts Persian phoneme error to 1.96%","feed_subtitle":"The system also disambiguates homographs and detects the unwritten Ezafe marker, in formal and informal Persian text.","key_machinery":"The load-bearing object is the Pinglish intermediate representation: a modified romanization of Persian with a one-to-one mapping from phonemes to Latin characters, obtained by excluding the ambiguous letters {c, u, q, w, x} and introducing special characters such as \"ķ\" for the Persian \"kh\" sound (as in \"ķAb\" for \"sleep\"). Around this sits a pipeline whose other moving parts are a rule-based post-processing step that enforces the one-to-one mapping, a custom BPE tokenizer (vocabulary 2,372, maximum subword length 3), and a five-layer encoder-decoder sequence-to-sequence model (8 attention heads, feed-forward dimension 1,024, hidden size 512) trained on Persian-to-Pinglish pairs. The machinery works by turning G2P into a closed-vocabulary transliteration task: because the target alphabet is small, consistent, and phonemically complete, the model can learn the mapping with far fewer labels than a direct Persian-to-IPA system would need.","core_discovery":"The central claim is that representing Persian pronunciation through a one-to-one phoneme-to-character intermediate language, rather than through IPA or direct Persian-to-IPA mapping, removes most of the ambiguity a neural model has to learn. The authors construct this intermediate (\"Pinglish\") by prompting a large language model with few-shot examples to romanize Persian sentences, then applying a rule-based post-processing step that eliminates ambiguous characters and assigns each Persian phoneme a unique Latin letter. A five-layer encoder-decoder transliteration model with a custom byte-pair-encoding tokenizer is then trained to map Persian graphemes to Pinglish. According to the paper, this yields a BLEU score of 94.6 and a phoneme error rate of 0.0196 on its own test set, with homograph disambiguation and Ezafe detection accuracy reported above the prior model on the SentenceBench corpus. The paper further claims the same intermediate-language recipe extends to Arabic and Chinese, which also have frequent homographs.","pith_inferences":["An unstated but immediate test is to score the same model on human-transcribed Persian phonemes or an independent pronunciation dictionary; that would separate genuine pronunciation accuracy from fidelity to the machine-generated labels.","A direct ablation the paper does not report is comparing the encoder-decoder with Pinglish targets against the same architecture with IPA targets; the paper's own rationale predicts Pinglish should win on PER, and that comparison would quantify the benefit of the intermediate representation.","The random train/test split of a single generated corpus leaves open the risk that repeated words or sentence templates appear in both halves; a word-disjoint or sentence-template-disjoint split would be a stronger test of generalization.","If the method is as language-agnostic as claimed, the same two-stage design could be applied to Arabic first, where vowel diacritics are also largely unwritten, and compared against existing Arabic G2P systems."],"forward_implications":["If the reported PER of 0.0196 is accurate, Persian text-to-speech preprocessing can use this compact transliteration pipeline with lower inference cost than dictionary-heavy or parsing-heavy G2P tools.","The paper's claimed superiority on SentenceBench implies the intermediate-language approach transfers to sentences with homographs and Ezafe better than LLM-prompting alone.","The data-generation recipe—LLM romanization plus rule-based normalization plus sentence merging—could cut the cost of building G2P training data for other low-resource languages.","The one-to-one phoneme-character design principle, if correct, predicts that extending the same framework to Chinese and Arabic would improve polyphone disambiguation in those languages."],"supporting_citations":[{"why":"Supplies the large language model used to generate the one million raw Persian sentences that seed the training corpus.","marker":"[11]"},{"why":"Provides the informal Persian podcast subtitles used alongside the generated formal sentences.","marker":"[16]"},{"why":"Is the sentence-level benchmark dataset on which the model's homograph disambiguation is compared.","marker":"[3]"},{"why":"Is the prior LLM-based Persian G2P approach that the paper compares against on SentenceBench.","marker":"[1]"},{"why":"Is the encoder-decoder model family used as the transliteration architecture.","marker":"[12]"},{"why":"Introduces byte-pair encoding, the tokenization algorithm the custom tokenizer is based on.","marker":"[23]"},{"why":"Adapts BPE with subword units for neural machine translation, motivating the tokenizer design.","marker":"[24]"},{"why":"Defines the BLEU metric used to report the 94.6 score.","marker":"[15]"},{"why":"Defines phoneme error rate, the primary accuracy metric.","marker":"[17]"},{"why":"Is the prior Persian G2P sequence-to-sequence system with 3.9 percent PER that the paper's reported PER is intended to beat.","marker":"[21]"}],"fun_headline_variants":["Pinglish cuts Persian phoneme error rate to 1.96%","Intermediate language tames Persian homograph G2P","Persian G2P error 1.96% via intermediate Latin script","Pinglish disambiguates polyphones and Ezafe in Persian"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing assumption is that the machine-created Pinglish labels used for training are correct pronunciations, because the model is trained and tested on random splits of that same machine-made data.","fun_headline_variants_meta":{"raw":{"variants":["Pinglish cuts Persian phoneme error rate to 1.96%","Intermediate language tames Persian homograph G2P","Persian G2P error 1.96% via intermediate Latin script","Pinglish disambiguates polyphones and Ezafe in Persian"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000637,"raw_usage":{"total_tokens":2962,"prompt_tokens":999,"completion_tokens":1963,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":615,"completion_tokens_details":{"reasoning_tokens":1887}},"tokens_in":615,"tokens_out":1963,"duration_ms":16692,"temperature":1.0,"reasoning_tokens":1887,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T22:37:46.722471+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a held-out set of Persian sentences that includes known homographs and Ezafe contexts, transcribe them into phonemes by hand or from an independent pronunciation dictionary, and compare the model's Pinglish output against those human transcriptions; if the phoneme error rate against human labels is materially higher than 0.0196, the reported accuracy is measuring agreement with the generator rather than pronunciation accuracy.","supporting_citations":[{"cited_title":"ﺷﯿﺮ\" is not considered a homograph in our study, despite having multiple meanings, because its pronunciation remains the same across different contexts. However, the word","cited_arxiv_id":null,"evidence_quote":"Is the sentence-level benchmark dataset on which the model's homograph disambiguation is compared."},{"cited_title":"ه -ﮐﺴﺮه\" and","cited_arxiv_id":null,"evidence_quote":"Is the prior LLM-based Persian G2P approach that the paper compares against on SentenceBench."}],"review_version":1}