Tokenization Failure Modes
Tokenization is a foundational step in natural language processing, but it is prone to several failure modes that can significantly affect downstream tasks and the quality of latent representations. Two of the most prevalent issues are boundary artifacts and language-specific weaknesses. Boundary artifacts occur when the tokenization process splits or merges text at inappropriate points, often due to simplistic or rigid rules that do not account for the complexities of language. For example, compound words, contractions, and punctuation can all lead to unexpected token boundaries. Language-specific issues arise when a tokenization algorithm, often designed with English or similar languages in mind, fails to handle the morphological richness or unique syntactic structures of other languages. This can result in the loss of important information, the introduction of noise, or the creation of tokens that do not correspond to meaningful linguistic units. These failure modes can distort the latent representations produced by subsequent models, leading to degraded performance in tasks such as classification, translation, or information retrieval. When token boundaries do not align with semantic or syntactic boundaries, models may struggle to capture the true meaning of text, and rare or out-of-vocabulary constructions may be ignored or misrepresented.
Boundary artifacts can be seen when tokenizing a compound word like notebook into note and book, which may not preserve the intended meaning. In morphologically rich languages such as Finnish or Turkish, a single word can encode complex grammatical structures (e.g., talossanikinko in Finnish, meaning "also in my house?"). Tokenizers not tailored to these languages may split such words into arbitrary or meaningless segments, losing critical information about case, possession, or question markers.
1234567891011121314151617181920212223242526272829303132333435import re # Simulate a simple whitespace and punctuation tokenizer def simple_tokenize(text): # Split on non-word boundaries (whitespace and punctuation) return re.findall(r'\w+', text) compound_word = "notebook" tokens = simple_tokenize(compound_word) print("Tokens:", tokens) # Output: Tokens: ['notebook'] # Now, let's simulate a naive subword tokenizer splitting compound words def naive_subword_tokenize(text): # Naively split on known subwords subwords = ['note', 'book'] tokens = [] start = 0 while start < len(text): matched = False for subword in subwords: if text.startswith(subword, start): tokens.append(subword) start += len(subword) matched = True break if not matched: # If no subword matches, add the character as a token tokens.append(text[start]) start += 1 return tokens tokens_subword = naive_subword_tokenize(compound_word) print("Naive Subword Tokens:", tokens_subword) # Output: Naive Subword Tokens: ['note', 'book']
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Tokenization is a foundational step in natural language processing, but it is prone to several failure modes that can significantly affect downstream tasks and the quality of latent representations. Two of the most prevalent issues are boundary artifacts and language-specific weaknesses. Boundary artifacts occur when the tokenization process splits or merges text at inappropriate points, often due to simplistic or rigid rules that do not account for the complexities of language. For example, compound words, contractions, and punctuation can all lead to unexpected token boundaries. Language-specific issues arise when a tokenization algorithm, often designed with English or similar languages in mind, fails to handle the morphological richness or unique syntactic structures of other languages. This can result in the loss of important information, the introduction of noise, or the creation of tokens that do not correspond to meaningful linguistic units. These failure modes can distort the latent representations produced by subsequent models, leading to degraded performance in tasks such as classification, translation, or information retrieval. When token boundaries do not align with semantic or syntactic boundaries, models may struggle to capture the true meaning of text, and rare or out-of-vocabulary constructions may be ignored or misrepresented.
Boundary artifacts can be seen when tokenizing a compound word like notebook into note and book, which may not preserve the intended meaning. In morphologically rich languages such as Finnish or Turkish, a single word can encode complex grammatical structures (e.g., talossanikinko in Finnish, meaning "also in my house?"). Tokenizers not tailored to these languages may split such words into arbitrary or meaningless segments, losing critical information about case, possession, or question markers.
1234567891011121314151617181920212223242526272829303132333435import re # Simulate a simple whitespace and punctuation tokenizer def simple_tokenize(text): # Split on non-word boundaries (whitespace and punctuation) return re.findall(r'\w+', text) compound_word = "notebook" tokens = simple_tokenize(compound_word) print("Tokens:", tokens) # Output: Tokens: ['notebook'] # Now, let's simulate a naive subword tokenizer splitting compound words def naive_subword_tokenize(text): # Naively split on known subwords subwords = ['note', 'book'] tokens = [] start = 0 while start < len(text): matched = False for subword in subwords: if text.startswith(subword, start): tokens.append(subword) start += len(subword) matched = True break if not matched: # If no subword matches, add the character as a token tokens.append(text[start]) start += 1 return tokens tokens_subword = naive_subword_tokenize(compound_word) print("Naive Subword Tokens:", tokens_subword) # Output: Naive Subword Tokens: ['note', 'book']
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