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Text of the page (random words):
asons or other things that are hard to pin down it s just that by focusing all our energy on a very particular convention newswire parliament we can pretty easily learn these mappings because there s no variability add some variability and we re hosed even for languages with the same set of overt markings posted by hal at 5 30 2014 01 31 00 pm 11 comments 16 may 2014 perplexity versus error rate for language modeling it s fair to say that perplexity is the de facto standard for evaluating language models perplexity comes under the usual attacks what does it mean does it correlate with something we care about etc but here i want to attack it for a more pernicious reason it locks us in to probabilistic models background language modeling or more specifically history based language modeling as opposed to full sentence models is the task of predicting the next word in a text given the previous words for instance given the history mary likes her coffee with milk and a good language model might predict sugar and a bad language model might predict socks this is related to the notion of cloze probability it s quite clear that there is no right answer to any of these prediction problem as an extreme example given the history the there are any number of possible words that could go next there s just no way to know what the right answer is whether you re a machine or a person this is probably the strongest justification for a perplexity like measure since there s no right answer we ll let our learned model propose a probability distribution over all possible next words we say that this model is good if it assigns high probability to sugar and low probability to socks perplexity just measures the cross entropy between the empirical distribution the distribution of things that actually appear and the predicted distribution what your model likes and then divides by the number of words and exponentiates after throwing out unseen words the issue the issue here is that in order to compute perplexity your model must produce a probability distribution historically we ve liked probability distributions because they can be combined with other probability distributions according to the rules of probability eg bayes rule or chain rule of course we threw that out a long time ago when we realized that combining things for instance in log linear models worked a lot better in practice if you had a bit of data to tune the weights of the log linear models so the issue in my mind is that there s plenty of good technology out there for making predictions that does not produce probability distributions i think it s really unfortunate that non probabilistic approaches don t get to play the language modeling game because they produce the wrong sort of output according to the evaluation but not according to the real world i m not saying there aren t good reasons to like probabilistic models but just that alternatives are good and right now those alternatives cannot compete for instance roark saraclar and collins 2007 don t use perplexity at all and just go for word error rate of a speech recognizer around their perceptron based language model when i ran into this i was curious about building a language model using vw in the context of another project and also to stress test multiclass classification algorithms that scale well with respect to the number of classes as soon as i ran it i discovered the issue it produced results in the form of error rates as i recall it was a while ago the error rate was somewhere in the 60s or 70s i had absolutely no idea whether this was good or not it seemed reasonable to get a sense of how standard language models fare i decided to train a language model using srilm and evaluate it according to error rate to make my life easier i just ran it on the wsj portion of the penn treebank i used the first 48k sentences as train and the last 1208 sentences as test i trained a 5gram kneser ney smoothed language model and evaluated both perplexity and error rate the latter required a bit of effort if anyone wants the scripts let me know and i ll post them but basically i just take the lm s prediction to be the highest probability word given the context the language model i built had a perplexity ppl1 in srilm of 236 4 which seemed semi reasonable though of course pretty crappy there was an oov rate of 2 5 ignored in the perplexity calculation the overall error rate for this model was 75 2 this means that it was only guessing a quarter of words correct note that this includes the 2 5 errors mandated by oovs i also tried another version where all the model had to do was put the words in the right order in other words it knows ahead of time the set of words in the sentence and just has to pick between those 20 rather than between the full vocabulary 43k types this is maybe semi reasonable for mt the error rate under this setting was 66 8 honestly i expected it would be a lot better note that if you always guess the most frequent type in this data your error rate is 95 3 so why was it only moderately helpful 10 improvement to tell the language model what the set of possible words was basically because the model was always guessing really high probability unigrams below are the top ten predicted words when the model made an error with their frequencies they re basically all stop words this is in the unrestricted setting 1 14722 2 1393 3 1298 the 4 512 and 5 485 in 6 439 of 7 270 to 8 163 9 157 a 10 108 is 11 54 s 12 52 have 13 49 said 14 41 15 38 16 38 for 17 38 are 18 34 19 33 20 31 be the same list for the restricted setting is virtually identical basically because most of these words are available in the average sentence 1 10194 2 5357 3 1274 the 4 274 of 5 251 in 6 232 to 7 230 and 8 193 a 9 51 10 36 for 11 28 s 12 25 13 24 said 14 24 is 15 21 that 16 21 17 16 it 18 15 from 19 14 be 20 13 by oh well ok so i don t have a good counter proposal to perplexity error rate certainly has many issues of its own you could use ir like metrics like recall 10 mean average precision etc which are all questionable in their own ways i would just in general like if we could evaluate language models without having to be handcuffed by probabilities posted by hal at 5 16 2014 03 54 00 pm 9 comments 26 april 2014 an easy way to write less hurtful reviews don t say you i ll be honest i ve had my feelings hurt by scathing reviews more than a few times in grad school i remember even crying over a review that i thought was particular pernicious my skin has thickened a bit over time though often in the not so helpful manner of dismissing reviews that i don t like as they didn t get it which defeats one of the two primary purposes of reviews in the first place providing feedback the other making accept reject decisions the thing that s hard to reconcile is that i really like most of the people in our community and everyone i meet at least seems really friendly when doing mock reviews with grad students i ll often tell them to keep in mind that there s a good chance that the author is or later will be a friend of theirs it s possible to provide feedback to a friend in such a way that you don t hurt their feelings i ve recently started doing something else in addition to the above suggestion i don t use the words you or the authors or even i the review of a scientific contribution is not about me and it s not about the authors it s about the method the experiments and the contribution i see little reason why you need to mention anything related to the people involved one exception i is often useful in hedging like the previous sentence which would be more forceful if i just said there is little reason perhaps we could even integrate this into start this is of course similar to the pop psych advice of talking to loved ones about actions rather than the person for instance i hate you for spilling coffee and not cleaning it up versus i hate having coffee spilt on the floor or something i m sure others can come up with better examples my current approach is to write my review with this in mind and then go back and search for all occurrences of my outlawed nouns and rewrite these sentences often in the process of doing this i become aware that in many of the cases what i ve said really does sound like an attack and with the very small edit this effect is removed or at least greatly reduced i realize i ve now just given a pretty good signal for people reading reviews to see if they were written by me or not here s a solution everyone should adopt this policy and then my reviews will no longer be so obvious but overall i really think we should be nice to each other perhaps fewer people will depart from the field if they re not constantly battered down by harsh reviews and then we ll all be better off posted by hal at 4 26 2014 03 03 00 pm 6 comments 14 april 2014 waaaah emnlp six months late okay so i ve had this file called emnlp txt sitting in my home directory since oct 24 last modification and since i want to delete it i figured i d post it here first i know this is super belated but oh well if anyone actually reads this blog any more you re the first to know how i felt 6 months ago i wonder if i would make the same calls today a log linear model for unsupervised text normalization yi yang and jacob eisenstein parsing entire discourses as very long strings capturing topic continuity in grounded language learning tacl minh thang luong michael c frank mark johnson document summarization via guided sentence compression chen li fei liu fuliang weng and yang liu sarcasm as contrast between a positive sentiment and negative situation ellen riloff ashequl qadir prafulla surve lalindra de silva nathan gilbert and ruihong huang identifying phrasal verbs using many bilingual corpora karl pichotta and john denero violation fixing perceptron and forced decoding for scalable mt training heng yu liang huang and haitao mi where not to eat predicting restaurant inspections from online reviews jun seok kang polina kuznetsova michael luca and yejin choi inducing document plans for concept to text generation ioannis konstas and mirella lapata powergrading a clustering approach to amplify human effort for short answer grading tacl sumit basu charles jacobs and lucy vanderwende if you remember anything about emnlp anymore and have your own opinions please feel free to comment it will also let me know if anyone reads here anymore happy spring from dc posted by hal at 4 14 2014 10 56 00 am 4 comments newer posts older posts home subscribe to posts atom about me hal view my complete profile labels acl 3 acs 2 advising 1 algorithms 2 bayesian 10 chunking 1 classification 1 clustering 3 community 26 conferences 45 coreference 1 data 2 discourse 3 domain adaptation 5 evaluation 9 finite state methods 1 graphical models 1 hiring 7 information retrieval 1 journals 3 language modeling 1 linguistics 7 loss functions 1 machine learning 45 machine translation 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