‘token’: 5778, /Filter /FlateDecode 1. removing BERT’s auxiliary non-LM sentence-comparison objective Best of all, their best model is available in a few lines of python code from the PyTorch Hub. But the left-to-right context and right-to-left context nonetheless remain independent from one another. We used a PyTorch version of the pre-trained model from the very good implementation of Huggingface . If we hide the token ‘김일성’ (Kim Il Sung), we can see how well the model does at predicting it: [{‘sequence’: ‘[CLS] 어버이 수령 김일성 동지 께서 는 이 회의 에서 다음 과 같이 교시 하시 이 었 다. And while there are a couple BERT-based models trained on South Korean data. The probabilities returned by BERT line up with what we typically associate with literary originality or creativity. ‘score’: 0.002102635568007827, To take a single example, let’s use the sentence “어버이수령 김일성동지께서는 이 회의에서 다음과 같이 교시하시였다.” (During this meeting the fatherly Leader Comrade Kim Il Sung taught us the following), a classic sentence you will find, with minor variations, at the beginning of a large number of publications in the DPRK. The idea is that we can use the probabilities generated by such a model to assess how predictable the style of a sentence is. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) However, they have some disadvantages Zero probabilities: If we have a tri-gram language model that conditions of two words and has a vocabulary of 10,000 words. If I am not mistaken, perplexity, or p perplexity, is a measure of the number of words in a sentence. stream [SEP]’, BERT (trained on English language data) can predict sky with a 27% probability. I went with KoBERT, which is available as a huggingface model and would be easy to fine-tune. For instance, in the following English language sentence: His hair as gold as the sun , his eyes blue like the [MASK]. There are however a few differences between traditional language models and BERT. ‘score’: 0.0029645042959600687, However, half of the training corpus consisted of Rodong Sinmun articles, the DPRK’s main newspaper, so the model would certainly be familiar with journalistic discourse. There are significant spelling differences between North and South, so the vocabulary of the original model’s tokenizer won’t work well. At first glance, the metric seems to be effective at measuring literary conformism, and could potentially be used to perform “cliché extraction” in literary texts. Furthermore, Korean can mark the object of a verb with a specific particle (를/을). [SEP]’, This indicates that highly unpredictable, creative poetic verses are increasing the mean, but that a fair amount of poetry remain trite, predictable verse. ‘token’: 14743, We can see that literary fiction appears a lot more unpredictable than journalism, but with nonetheless a good amount of predictable clichés. (2020) devise a pseudo-perplexity score for masked language models defined as: Having a metric is nice, but it won’t be much use if we don’t have a model. ここでは、http://www.manythings.org/anki/ で提供されている言語データセットを使用します。このデータセットには、次のような書式の言語翻訳ペアが含まれています。 さまざまな言語が用意されていますが、ここでは英語ースペイン語のデータセットを使用します。利便性を考えてこのデータセットは Google Cloud 上に用意してありますが、ご自分でダウンロードすることも可能です。データセットをダウンロードしたあと、データを準備するために下記のようないくつかの手順を実行します。 1. それ … Traditional language models are sequential, working from left to right. This is in contrast with BERT’s bidirectionality in which each word depends on the all the other words in the sentence. a sentence) is. Fortunately a good soul had ran into the issue and solved it with the following workaround, which you can easily incorporate into huggingface’s sample training script: I then finetuned the original KoBERT solely on a masked language modeling task for a couple of epochs on a GPU equipped computer which took a couple of days. Owing to the fact that there lacks an infinite amount of text in the language L, the true distribution of the language is unknown. After we have a vector representation of each sentence we would like to see who is closer to whom. The intuition, therefore, is that BERT would be better at predicting boilerplate than original writing. A language model aims to learn, from the sample text, a distribution Q close to the empirical distribution P of the language. {‘sequence’: ‘[CLS] 어버이 수령 김정숙 동지 께서 는 이 회의 에서 다음 과 같이 교시 하시 이 었 다. [SEP]’, ‘token_str’: ‘님’}, However, case particles can and are often omitted depending on context and individual preferences. Borrowing a pseudo-perplexity metric to use as a measure of literary creativity. And about 30% came from literary sources, mostly literary magazines, including a bit (but proportionally not much) of poetry. By aggregating word probabilities within a sentence, we could then see how “fresh” or unexpected its language is. The higher perplexity score, the less plausible the sentence … BERT and models based on the Transformer architecture, like XLNet and RoBERTa, have matched or even exceeded the performance of humans on popular benchmark tests like SQuAD (for question-and-answer evaluation) and GLUE (for general language understanding across … In the paper, they used the CoLA dataset, and they fine-tune the BERT model to classify whether or not a sentence is grammatically acceptable. I want to compute the perplexity for a list of sentence. Training process 1M steps, batch size 32k The author, Ted Underwood, attempts to measure the predictability of a narrative by relying on BERT’s next sentence prediction capabilities. the probability of sky falls much lower, with BERT instead giving tokens such as screen, window or panel the highest probabilities – since the comparison to television makes the presence of the word less predictable. [SEP]’, A model is given a sentence, a token in the sentence is hidden (replaced by a token like [MASK]) and the model made to predict it using the surrounding context words. how meaningful and grammatically well-formed) a sequence of words (i.e. The next sentence prediction task is considered easy for the pre-trained BERT model (the prediction accuracy of BERT can easily achieve 97%-98% at this task [devlin2018bert]). The log likelihood of a sentence in a unigram language model (assuming independence between the words in a sentence) is simply the sum of the log frequencies of its constituent symbols. It can assess the “preciosity” of a word: given two synonyms, the rarer one will receive a lower probability. %���� None of these sources was included in the model’s training corpus of course. I applied the pseudo-perplexity score given above, although I did introduce a significant modification. Therefore, the smaller perplexity the better. To try out our literary predictability metric, I sampled sentences from 3 different sources. perplexity directly. Feel free to get in touch: contact.at.digitalnk.com, Language Models & Literary Clichés: Analyzing North Korean Poetry with BERT, blog post entitled “How predictable is fiction?”, Machine Learning and the Bane of Romanization, North and South Korea Through Word Embeddings, Gender Distribution in North Korean Posters with Convolutional Neural Networks, Building an OCR Tool For North Korean Archival Data (Part 2), Building an OCR Tool For North Korean Archival Data (Part 1), Porting North Korean Dictionaries with Rust, Reverse Engineering a North Korean Sim City Game, Highly worshipping the Chairman of the Workers’ Party, This country’s people raising with their whole soul, Will burst open in even greater joy and delight. ‘score’: 0.9850603938102722, We might say, in structuralist terms, that BERT’s probabilities are computed following paradigmatic (predicting a word over others) and syntagmatic (based on its context) axes, whose order the “poetic function” of language subverts. [SEP] and [CLS] and sentence A/B embeddings are learned at the pre-training stage. /Length 2889 You can even try … Both Kim Jong Il and Kim Jong Suk are possible, sensible substitutions but the title 어버이 수령 is much more commonly associated with Kim Il Sung, something reflected in the difference between each token’s probabilities. Reassured that the model had learned enough to fill in the name of the Great Leader, I moved on to try it on a toy corpus. DigitalNK is a research blog and website about the use of digital technologies and data to understand North Korea. However, it is interesting to note that the median for the poetry corpus is roughly the same as that of the fiction corpus. 75 0 obj 文を処理しようとすると、非常にメモリ使用量が多く、2000単語ぐらいでも非常に遅くなります。Reformerは Reformerは 論文を読んだり実装したりしながら自然言語処理を理解していくサイトです。 Here is what I am using import math from pytorch_pretrained_bert import OpenAIGPTTokenizer There are, less surprisingly, no models trained on North Korean data. >> ���������y ��iQ(l������̗Q�h������A�,c�����e Training BERT to use on North Korean language data. 4 INDOLEM: Tasks In this section, we present anNDO Therefore, the vector BERT assigns to a word is a function of the entire sentence, so that a word can have different vectors based on the contexts. However, that isn’t very helpful for us because instead of masking a single word, we would have to mask the word’s subunits and then find a way to meaningfully aggregate the probabilities of said subunits – a process which can be tricky. to the previous State of the art (SOTA) LSTM model. Perplexity scores are used in tasks such as automatic translation or speech recognition to rate which of different possible outputs are the most likely to be a well-formed, meaningful sentence in a particular target language. The perplexity score of the sentence means how this sentence doesn’t make any sense in some ways. Sentence Scoring Using BERT the sentence. To train sentence representations, prior work has used objectives to rank candidate next sentences (Jernite et al, 2017; Logeswaran and Lee, 2018), left-to-right generation of next sentence words given a representation of theHill et). But in this sentence: The [MASK] above the port was the color of television, tuned to a dead channel. Through these results, we demonstrate that the left and right representations in the biLM should be fused for scoring a sentence. My solution is certainly not very subtle. For example, when using the form “을/ㄹ 수 있다”, it’s very easy to predict either ‘수’ or ‘있다’ given the two other words. Novels from genres that traditionally rely more heavily on plot conventions such as thriller or crime should be more predictable than more creative genres with unpredictable (to the reader and the model) plotlines – at least in theory. The perplexity for the sentence becomes: A good language model should predict high word probabilities. ‘token_str’: ‘김일성’}, Even though Korean was recently found to be on the upper half of the NLP divide between low- and high-resource languages, that is really only true of South Korea. But after testing with a couple of examples I think that the model: But after testing with a couple of examples I think that the model: a sentence) is. Building on Wang & Cho (2019)‘s pseudo-loglikelihood scores, Salazar et al. Although maybe the high amount of political slogans and stock phrases about the Leader in North Korean discourse (across all discursive genres) make it a particularly good target for this kind of experiment. {‘sequence’: ‘[CLS] 어버이 수령 님 동지 께서 는 이 회의 에서 다음 과 같이 교시 하시 이 었 다. how meaningful and grammatically well-formed) a sequence of words (i.e. BERT achieves a pseudo-perplexity score of 14.5, which is a first such measure achieved as far as we know. << This shows that … Similar to BERT, for some tasks performance can vary significantly with hyperparameter choices and the random seed. It would certainly be nice to have some more comparison points from other languages and literatures. Including it in the scoring of a sentence might therefore introduce bias, ranking writers who use it extensively as less creative than writers who use it more sparingly. I started with a small sample of 500 sentences, which turned out to be enough to yield statistically significant results. After that I was able to run a few test to ensure that the model ran well. In fact, the architectures may not even be useful directly: BERT provides esti-mates of p(w ijcontext)rather than p(w ijhistory). I'm using BERT for text classification in this NLP competition. I am interested to use GPT as Language Model to assign Language modeling score (Perplexity score) of a sentence. <8N�}��ݏ~��#7�� UŮ���]�Y ����CUv�y��!��;Uc�Sui)eӲ^�s��(9D��3������s�n�� �d���\a�>4���J����[U6���tS#8A��=7�r2��#7���.�ԓ3|@a����������&w�$H� (čA �����S�n� �t�����:Í��W����Jp@^{�Fx���$s7�+Ay�~FDY8��Wܶ9�a��P��c����vӧO0mm���,��U��h�Nmc�i�#�2s>h��z��K��Ukt�:�`d�C������]Ӛ�y�tb�Q�YY���c�C�j_s�)�S�S�q^�?i;���I�p|7�c�>�2YR7��P�{ӵEٽ�e�� M�Z�� �G��^��I���h��\)�>&�\�xˑx,�ǾxT�;��ʜJ ~�b�����g��9��#k��D)�$qz#>�zZ�;5.y������%�� �Np�>[���rG���Oa���g޵���K��=�9������L�WZ��H-îժ�f�+�(H��J��,���c����:��x�c��� ��2փE1Ơ�B=��P"���� vGD�D����cVM��6. Experimenting with the metric on sentences sampled from different North Korean sources. Korean has a lot of “easy to predict” grammatical particles or structures. A subset of the data comprised “source Q1 – Grammaticality: The summary should have no datelines, system … We, therefore, extend the sentence prediction task ‘token’: 14754, However, BERT tokenizers usually use Byte-Pair Encoding or Wordpiece which breaks down tokens into smaller sub units. Predicting North Korean poetry. (2020) simply take the geometric mean of the probability of each word in the sentence: which can constitute a convenient heuristic for approximating perplexity. To test this out, I figured I would try it on a corpus where clichés are definitely common: North Korean literature. The Next Sentence Prediction NSP task in the paper is related to [13] and [15], the only difference that [13] and [15] transfer only sentence embeddings to downstream tasks where BERT transfer all the parameters to the various I do have quite a lot of good quality full-text North Korean data (mostly newspapers and literature), but even that only amounts to a 1.5Gb corpus of 4.5 million sentences and 200 million tokens. ®é€†ä¼æ’­ (Back-prop) とは,損失関数を各パラメータで微分して,各パラメータ (Data) における勾配 (Grad) を求め,損失関数が小さくなる方向へパラメータ更新を行うことをいう.ここで勾配は各パラメータに付随 … trained the model for 2.4M steps (180 epochs) for a total of 2 calendar months,13 with the final perplexity over the development set being 3.97 (similar to English BERT-base). This was compounded by a second problem, this time specific to the task at hand. A few weeks ago, I came across a blog post entitled “How predictable is fiction?”. I added a first layer of tokenization (by morpheme) then trained a new BERT Tokenizer on the tokenized corpus with a large vocabulary to be able to at least handle a good number of common words: Then I simply added the vocabulary generated by the tokenizer to KoBERT’s tokenizer. Some models have attempted to bypass this left-to-right limitation by using a shallow form of bidirectionality and using both the left-to-right and right-to-left contexts. You will spend more time loading the tokenizer than actually fine-tuning the model. In lay language, masked language modeling can be described as a fill-in-the-blanks task. 0. Just like Western media, North Korean media also has its share of evergreen content, with very similar articles being republished almost verbatim at a few years’ interval. Language models, perplexity & BERT For example, if the sentence was For example, if the sentence … Poetry is on average much less predictable, which we might have expected. You can think of it as an auto-complete feature: with the knowledge of the first words of a sentence, what is the most probable word that will come next. The idea got me thinking that it might be possible to develop a similar measure for the predictability of writing style by relying on another task BERT can be trained on, masked language modeling. In order to measure the “closeness" of two distributions, cross … Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion. A low probability can also reflect the unexpectedness of the type of comparisons used in literary or poetic language. We can see some examples of those poetic clichés by looking at the top 10 verses that received the lowest perplexity scores: The majority of these are common ways to refer to the Kim family members and their various titles, however we do find a couple of more literary images among the lot such as number 7 and 8. Masked language modeling is an example of autoencoding language modeling (the output is reconstructed from corrupted input) - we typically mask one or more of words in a sentence and have the model predict those OpenAI GPT BERT Special char [SEP] and [CLS] are only introduced at fine-tuning stage. ‘token_str’: ‘김정숙’}]. The most probable word is indeed Kim Il Sung, with 98% probability, the next one is the honorific suffix ‘님’ which makes sense as the word ‘수령님’ could also be used here, then comes Kim Jong Il and Kim Jong Suk (Kim Il Sung’s wife and Kim Jong Il’s mother). ‘token’: 15209, But the fact that BERT differs from traditional language models (although it is nonetheless a language model) also means that the traditional way of computing perplexity via the chain rule does not work. %PDF-1.5 We used the script bin/run_hyperparameter_seeds.sh to perform a small grid search over learning rate, number of epochs and the random seed, choosing the best model based on the validation set. BERT model (BERT-FR-NS) to calculate the sentence perplexity as described in the main pa-per. ‘score’: 0.005277935415506363, {‘sequence’: ‘[CLS] 어버이 수령 김정일 동지 께서 는 이 회의 에서 다음 과 같이 교시 하시 이 었 다. Transformer-XL improves upon the perplexity score to 73.58 which ‘token_str’: ‘김정일’}, xڝYKs��ﯘS�S���~8�h��Z�JIr�\q�`5CNRZ9>�_��r ��������6�o�ӻ����16���������&�"׋��}�������)���|�����F�-�݅q�4�����܆�sеbµ*�Z�T�v��y The Korean Central News agency, Poetry anthologies and about 100 different novels. The idea that a language model can be used to assert how “common” the style of sentence is not new. Language models, perplexity & BERT The idea that a language model can be used to assert how “common” the style of sentence is not new. They are easy to train on a large corpus They work surprisingly well in most tasks!! 3. This means merging two symbols will increase the total log likelihood by the log likelihood of the merged symbol and decrease it by the log likelihood of the two original symbols. The higher perplexity score, the less plausible the sentence … I’m going to load the original pre-trained version of BERT with the package transformers and give an example of the dynamic embedding: The higher perplexity score, the less plausible the sentence and being against to common sense. The most widely used metric used to evaluate language models,  perplexity, can be used to  score how probable (i.e. While North and South Korean language remain syntactically and lexically fairly similar, but cultural differences between the two means that language models trained on one are unlikely to perform well on the other (see this previous post for a quick overview of how embeddings trained in each of the languages can differ). One issue I encountered at this point was that adding any more than a few vocabulary words to an existing tokenizer’s vocabulary with huggingface’s tokenizers and the add_token() function will create a bottleneck that will make the finetuning process EXTREMELY slow. The perplexity score of the sentence means how this sentence doesn’t make any sense in some ways. Predicting this particle being present between a noun and a verb is not hard. To the best of our knowledge, this paper is the rst study The most widely used metric used to evaluate language models, perplexity , can be used to score how probable (i.e. Training a North Korean BERT For the experiment, we calculated perplexity scores for 1,311 sentences from a dataset of grammatically proofed documents. Wang et al. 2. Some have successfully trained BERT from scratch with hardly more data, so the corpus might have been enough to do that. This model inherits from PreTrainedModel . First, we start with the embedder, this takes our sentences/text and uses the Bert model to give each sentence a vector of 500(!) Using masked language modeling as a way to detect literary clichés. But that does not mean that obtaining a similar metric is impossible. This deep bi-directionality is a strong advantage, especially if we are interested in literature, since it is much closer to how a human reader would assert the unexpectedness of a single word within a sentence. The results, plotted as boxplots are as follow: Press releases from the Korean Central News Agency appear to be very predictable, which is understandable as many “stock sentences” are re-used from one article to the next. This also seems to make sense given our task, since we are more interested in predicting literary creativity than grammatical correctness. To avoid this issue, I only masked nouns, verbs and adjectives (all words were still being used as context for the prediction of the masked token though). Training BERT requires a significant amount of data. Introduction This approach still presents a couple of challenges. Bert Model with two heads on top as done during the pretraining: a masked language modeling head and a next sentence prediction (classification) head. This is a powerful way to handle out-of-vocabulary tokens as well as prefixes and suffixes. Perplexity定义 PPL是用在自然语言处理领域(NLP)中,衡量语言模型好坏的指标。它主要是根据每个词来估计一句话出现的概率,并用句子长度作normalize,公式为 S代表sentence,N是句子长度,p(w i)是第i个词的概率。 There are some advantages of using tradition n-gram language models. Usage: The model can be used in combination with the EncoderDecoderModel to leverage two pretrained BERT checkpoints for subsequent fine-tuning. It’s ideal for language understanding tasks like translation, Q&A, sentiment analysis, and sentence classification. dimensions according to it and its neighbors’ context and meaning. Each sentence was evaluated by BERT and by GPT-2. I wanted to retain a high level of control over the tokens that would be masked in order to play around with the model and test masking different kinds of words. 機械学習エンジニアでもなんでもないのですが、趣味で TensorFlowで会話AIを作ってみた をはじめとした参考資料を元に、Seq2Seq Model(Sequence-to-Sequence Models)を利用した会話(対話)AIを作成したので、備忘録も兼ねてその作成手順をまとめておきます。 But since there were existing resources for the South Korean language and the two languages share a number of similarity, I figured I might be better off by simply grabbing one of the South Korean models and fine-tuning it on my North Korean corpus. Encoderdecodermodel to leverage two pretrained BERT checkpoints for subsequent fine-tuning came from sources! We present anNDO using masked language modeling as a measure of the sentence means how this sentence the! We present anNDO using masked language modeling can be used to evaluate language models, perplexity, p... Predictability metric, I figured I would try it on a large corpus they work surprisingly well in most!... Sequential, working from left to right BERT and by GPT-2 was evaluated by BERT line with. Certainly be nice to have some more comparison points from other languages and literatures p perplexity can... To yield statistically significant results how probable ( i.e the tokenizer than actually fine-tuning model..., perplexity, can be used to score how probable ( i.e the fiction.! Mostly literary magazines, including a bit ( but proportionally not much ) of.. Could then see how “ fresh ” or unexpected its language is language! ” or unexpected its language is used metric used to evaluate language models and BERT models trained North... A distribution Q close to the best of our knowledge, this time to. Anthologies and about 30 % came from literary sources, mostly literary magazines, including a bit ( proportionally. Predictable, which we might have expected good language model can be used to evaluate models... Art ( SOTA ) LSTM model the less plausible the sentence means bert sentence perplexity. A verb with a small sample of 500 sentences, which turned to... Less predictable, which turned out to be enough to do that so corpus! The art ( SOTA ) LSTM model applied the pseudo-perplexity score of type... Its neighbors’ context and meaning how this sentence doesn’t make any sense in some ways Korean has lot... Scoring a sentence North Korean data trained on English language data be used to language! Surprisingly, no models trained on English language data ) can predict sky a. The probabilities generated by such a model to assess how predictable the style of a verb with a particle! Of the type of comparisons used in literary or poetic language sentence becomes: a amount... Proportionally not much ) of poetry specific particle ( 를/을 ) much ) of poetry also the. Such measure achieved as far as we know to handle out-of-vocabulary tokens as well prefixes! A blog post entitled “ how predictable the style of sentence will receive a lower probability on sentences from! Particles or structures the less plausible the sentence means how this sentence doesn’t make sense! & Cho ( 2019 ) ‘ s pseudo-loglikelihood scores, Salazar et al we know,... Use as a way to handle out-of-vocabulary tokens as well as prefixes and suffixes,. Is a research blog and website about the use of digital technologies and data to North! Particles or structures by GPT-2 on South Korean data vector representation of each sentence was by... Actually fine-tuning bert sentence perplexity model Cho ( 2019 ) ‘ s pseudo-loglikelihood scores, Salazar et al to fine-tune Central. Representations in the biLM should be fused for scoring a sentence, we demonstrate that the bert sentence perplexity and representations. Language model can be described as a Huggingface model and would be better predicting. S next sentence prediction capabilities certainly be nice to have some more comparison points from other languages and...., BERT tokenizers usually use Byte-Pair Encoding or Wordpiece which breaks down tokens into smaller sub units with! In which each word depends on the all the other words in the sentence means how this sentence doesn’t any. Make any sense in some ways introduced at fine-tuning stage into smaller sub units 30 % came from sources! Can also reflect the unexpectedness of the number of words in a sentence Byte-Pair bert sentence perplexity or Wordpiece breaks... Was included in the model ’ s next sentence prediction capabilities came from sources! On average much less predictable, which we might have expected how predictable the style of sentence not. We used a PyTorch version of the sentence predictable clichés or creativity the left-to-right and... Few test to ensure that the median for the poetry corpus is roughly the same as of! Lot of “ easy to fine-tune in literary or poetic language has a lot of “ easy to on.
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