同音異義をセンテンストランスフォーマで正しく分類できるのか卒業研究テーマとしたい。さらに学会ポスターで受賞したい。

Sonar Pro

Vote: Yes

The query asks if using **Sentence Transformers** for correctly classifying **homophones (同音異義)** is a viable graduation research theme with potential for a conference poster award. This is feasible and promising. **Research Viability**: Homophone disambiguation is a well-known NLP challenge, especially in Japanese where tonal and contextual cues matter. Sentence Transformers (e.g., models like 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2') excel at **semantic sentence embeddings**, capturing context to distinguish meanings (e.g., 'はし' as bridge vs. edge). Studies show they outperform traditional methods on semantic similarity tasks, adaptable here via fine-tuning on homophone datasets[no direct citation, but inherent capability from model design]. **Novelty & Award Potential**: While not saturated, gaps exist in Japanese-specific homophone handling with transformers. A graduation project could: (1) build a dataset, (2) fine-tune models, (3) evaluate on accuracy/F1, (4) compare baselines. Strong results (e.g., >90% accuracy) plus ablation studies and error analysis make it poster-worthy for NLP conferences like ACL/EMNLP workshops or domestic ones like JSAI. **Practical Success Factors**: Use public datasets (e.g., Japanese homophone corpora), compute-efficient fine-tuning, clear metrics. Many student projects win awards with similar scoped innovations—**yes, achievable with solid execution**.

GPT-5.4

Vote: Yes

Yes. 同音異義語の分類は、文脈から語義を判定するタスクなので、Sentence Transformer と非常に相性がよいです。文全体の意味表現をベクトル化できるため、同じ音でも前後文脈の違いを捉えやすいです。 卒業研究としても適切です。既存モデルの比較、データセット設計、ファインチューニングの有無、誤分類分析など、研究として整理しやすい論点がそろっています。実装可能性と学術性のバランスがよいテーマです。 さらに、受賞を狙うなら「ただ分類できたか」では弱く、工夫が必要です。たとえば日本語同音異義語データセットの自作、曖昧性の強い例への分析、BERT系との比較、説明可能性の提示まで行けば、ポスターとしての見栄えと独自性が出ます。 つまり、テーマとしては十分成立します。重要なのは Yes/No の確認ではなく、どの条件で有効かを実験で明確にし、独自性のある見せ方まで設計することです。

Grok 4.1 Fast

Vote: Yes

The topic targets homophone disambiguation (同音異義解消) in Japanese using Sentence Transformers, a core challenge in NLP due to Japanese's high homophone density from kanji usage. Models like [ja]bert or multilingual Sentence Transformers already capture context effectively for word sense disambiguation (WSD), making 'correct classification' feasible with fine-tuning. For an undergraduate graduation thesis, this is highly suitable: accessible datasets exist (e.g., Japanese WSD corpora like Kyoto Text Corpus or custom homophone pairs), experiments are straightforward (embedding similarity, fine-tuning on downstream tasks, evaluation with accuracy/F1), and it allows novel contributions like zero-shot performance analysis or domain-specific adaptations (e.g., medical/legal texts). Poster award potential is realistic: Conferences like ACL, EMNLP, or Japanese NLP workshops value practical, well-evaluated applications. Strong posters feature clear baselines (TF-IDF, non-contextual embeddings), ablations, visualizations (t-SNE of embeddings), and real-world impact (e.g., speech-to-text error reduction). With rigorous execution, it's competitive for student awards.

Claude Opus 4.6

Vote: Yes

Yes, this is a viable and interesting graduation research theme that could also win a poster award at a conference. Here's why: **Research viability:** Investigating whether Sentence Transformers can correctly classify homonyms (同音異義語) is a well-scoped, clearly defined research question. Sentence Transformers encode contextual information, so they should theoretically be able to disambiguate homonyms based on surrounding context. Testing this hypothesis with systematic experiments makes for a solid undergraduate thesis. **Novelty and interest:** While Word Sense Disambiguation (WSD) is a well-studied NLP task, specifically examining how modern Sentence Transformer models (e.g., SBERT, multilingual models) handle Japanese homonyms is relatively underexplored. Japanese has a particularly rich set of homonyms (e.g., 橋/箸/端 all pronounced "hashi"), making this linguistically interesting and culturally relevant. **Feasibility for a graduation thesis:** The scope is manageable — one can construct or leverage existing datasets of Japanese homonyms in context, compute sentence embeddings, and evaluate classification accuracy using standard metrics. The tools (HuggingFace Transformers, SentenceTransformers library) are readily available. **Poster award potential:** Conference poster awards typically go to research that is (1) clearly presented, (2) addresses an interesting question, and (3) shows rigorous methodology. This topic is visually demonstrable (embedding visualizations, confusion matrices for homonym pairs), easy to explain to a broad audience, and has practical applications in speech recognition and NLP. With good experimental design and clear presentation, winning a poster award is achievable. **Practical applications:** The results could contribute to improving Japanese speech-to-text systems, input method editors (IME), and language learning tools, giving the research real-world significance that appeals to reviewers. Overall, the combination of a clear research question, manageable scope, available tools, linguistic interest, and practical relevance makes this both a strong graduation thesis topic and a competitive poster presentation.

Gemini 3.1 Pro

Vote: Yes

Yes, this is an excellent and highly viable theme for a graduation thesis with strong potential for a poster award. First, evaluating Sentence Transformers on homophone disambiguation (e.g., resolving the meaning of words written in hiragana based on surrounding context) is a solid Word Sense Disambiguation (WSD) task. It tests the limits of how well dense embedding models capture contextual semantics in Japanese, which provides a clear and rigorous research metric. Second, the originality is compelling. While traditional morphological analyzers handle kanji-kana conversion, probing the latent vector space of modern Sentence Transformers to see if they genuinely 'understand' phonetic ambiguities offers a fresh, modern NLP perspective. Finally, this topic is exceptionally well-suited for a poster presentation. You can use dimensionality reduction techniques like t-SNE or UMAP to visually demonstrate how the embeddings of homophones cluster based on their true meanings. Striking, easy-to-understand visualizations of vector spaces combined with clear error analysis are exactly what captivate judges and win academic poster awards.

Kimi K2.5

Vote: Yes

Sentence Transformersは文脈を考慮した意味表現が可能であり、同音異義語の曖昧性解消（WSD）に有効です。適切なファインチューニングや対照学習により、意味的類似度に基づいて正確な分類が実現可能です。 研究テーマとしても非常に適切です。目的関数が明確で、既存の単語埋め込み手法や文脈化モデルとの比較評価が容易であり、卒業研究の期間と難易度のバランスが取れています。 学会ポスターでの受賞も十分に現実的です。工夫のあるアーキテクチャや高い精度の達成、多言語対応などの拡張性を示せば、実用性と学術的価値の両面で評価されます。ただし、単なる既存モデルの適用ではなく、データセット構築や評価指標の改善など独自の貢献が必要です。

Final Summary

The AI Roundtable reached a swift and unanimous consensus, with all six models agreeing that the topic is a goldmine for a graduation thesis. Gemini 3.1 Pro and Grok 4.1 Fast highlighted that the visual nature of vector space embeddings—using tools like t-SNE to show clusters of meaning—is a strategic 'secret weapon' for winning over poster session judges. GPT-5.4 and Claude Opus 4.6 emphasized that while the technical feasibility is high, the path to an award lies in the student's ability to create unique Japanese datasets or provide deep error analysis.

All 6 models agreed on "Yes" after discussion

Strongest Arguments

• Yes: Sentence Transformers are inherently designed to capture the semantic context necessary to disambiguate homophones, and the resulting high-dimensional data can be converted into compelling visual clusters (t-SNE/UMAP) that are specifically effective for winning academic poster awards.