1. The mental representation of sentences Tree structures or state vectors? Stefan Frank S.L.Frank@uva.nl

2. With help from Rens Bod Victor Kuperman Brian Roark Vera Demberg

3. Understanding a sentence The very general picture the cat is on the mat word sequence “meaning” “comprehension”

4. Sentence meaning Theories of mental representation  x,y : cat (x) ∧ mat (y) ∧ on (x,y) tree structure cat on mat logical form conceptual network perceptual simulation state vector or activation pattern Sentence “structure” … ?

5. Grammar-based vs. connectionist models  Grammars account for the productivity and systematicity of language (Fodor & Pylyshyn, 1988; Marcus, 1998)  Connectionism can explain why there is no (unlimited) productivity and (pure) systematicity (Christiansen & Chater, 1999) The debate (or battle) between the two camps focuses on particular (psycho-) linguistic phenomena, e.g.:

6. From theories to models Probabilistic Context- Free Grammar (PCFG) Simple Recurrent Network (SRN) versus  Implemented computational models can be evaluated and compared more thoroughly than ‘mere’ theories  Take a common grammar-based model and a common connectionist model  Compare their ability to predict empirical data (measurements of word-reading time)

7. Probabilistic Context-Free Grammar  A context-free grammar with a probability for each production rule (conditional on the rule’s left-hand side)  The probability of a tree structure is the product of probabilities of the rules involved in its construction.  The probability of a sentence is the sum of probabilities of all its grammatical tree structures.  Rules and their probabilities can be induced from a large corpus of syntactically annotated sentences (a treebank).  Wall Street Journal treebank: approx. 50,000 sentences from WSJ newspaper articles (1988−1989)

8. Inducing a PCFG S NPVP. PRP VPZNP PP DT NN NP PRP$ IN NP. It has nobearing on our work forcetoday S  NP VP. NP  DT NN NP  PRP$ NN NN NN  bearing NN  today

9. Simple Recurrent Network Elman (1990)  Feedforward neural network with recurrent connections  Processes sentences, word by word  Usually trained to predict the upcoming word (i.e., the input at t+1) word input at t hidden layer output layer hidden activation at t–1 (copy) state vector representing the sentence up to word t estimated probabilities for words at t+1

10. Word probability and reading times Hale (2001), Levy (2008) surprisal of w t  Surprisal theory: the more unexpected the occurrence of a word, the more time needed to process it. Formally:  A sentence is a sequence of words: w 1, w 2, w 3, …  The time needed to read word w t, is logarithmically related to its probability in the ‘context’: RT(w t ) ~ −log Pr(w t |context)  If nothing else changes, the context is just the sequence of previous words: RT(w t ) ~ −log Pr(w t |w 1, …, w t−1 )  Both PCFGs and SRNs can estimate Pr(w t |w 1, …, w t−1 )  So can they predict word-reading times?

11. Testing surprisal theory Demberg & Keller (2008)  Reading-time data:  Dundee corpus : approx. 2,400 sentences from The Independent newspaper editorials  Read by 10 subjects  Eye-movement registration  First-pass RTs: fixation time on a word before any fixation on later words  Computation of surprisal:  PCFG induced from WSJ treebank  Applied to Dundee corpus sentences  Using Brian Roark’s incremental PCFG parser

12. Testing surprisal theory Demberg & Keller (2008) But accurate word prediction is difficult because of  required world knowledge  differences between WSJ and The Independent: − 1988-’89 versus 2002 − general WSJ articles versus Independent editorials − American English versus British English − only major similarity: both are in English Result: No significant effect of word surprisal on RT, apart from the effects of Pr(w t ) and Pr(w t |w t  1 ) Test for a purely ‘structural’ (i.e., non-semantic) effect by ignoring the actual words

13. S NPVP. PRP VPZNP PP DT NN NP PRP$ IN NP. no on today it has bearing our work force part-of-speech (pos) tags

14.  ‘Unlexicalized’ (or ‘structural’) surprisal −PCFG induced from WSJ trees with words removed −surprisal estimation by parsing sequences of pos-tags (instead of words) of Dundee corpus texts −independent of semantics, so more accurate estimation possible −but probably a weaker relation with reading times  Is a word’s RT related to the predictability of its part- of-speech?  Result: Yes, statistically significant (but very small) effect of pos-surprisal on word-RT Testing surprisal theory Demberg & Keller (2008)

15. Caveats  Statistical analysis: −The analysis assumes independent measurements −Surprisal theory is based on dependencies between words −So the analysis is inconsistent with the theory  Implicit assumptions: −The PCFG forms an accurate language model (i.e., it gives high probability to the parts-of-speech that actually occur) −An accurate language model is also an accurate psycholinguistic model (i.e., it predicts reading times)

16. Solutions  Sentence-level (instead of word-level) analysis −Both PCFG and statistical analysis assume independence between sentences −Surprisal averaged over pos-tags in the sentence −Total sentence RT divided by sentence length (# letters)  Measure accuracy a)of the language model: lower average surprisal  more accurate language model b)of the psycholinguistic model: RT and surprisal correlate more strongly  more accurate psycholinguistic model  If a) and b) increase together, accurate language models are also accurate psycholinguistic models

17. Comparing PCFG and SRN  PCFG −Train on WSJ treebank (unlexicalized) −Parse pos-tag sequences from Dundee corpus −Obtain range of surprisal estimates by varying ‘beam-width’ parameter, which controls parser accuracy  SRN −Train on sequences of pos-tags (not the trees) from WSJ −During training (at regular intervals), process pos-tags from Dundee corpus, obtaining a range of surprisal estimates  Evaluation −Language model: average surprisal measures inaccuracy (and estimates language entropy) −Psycholinguistic model: correlation between surprisals and RTs just like Demberg & Keller

18. PCFG SRN Results

19. Preliminary conclusions  Both models account for a statistically significant fraction of variance in reading-time data.  The human sentence-processing system seems to be using an accurate language model.  The SRN is the more accurate psycholinguistic model. But PCFG and SRN together might form an ever better psycholinguistic model

20. Improved analysis  Linear mixed-effect regression model (to take into account random effects of subject and item)  Compare regression models that include: −surprisal estimates by PCFG with largest beam width −surprisal estimates by fully trained SRN −both  Also include: sentence length, word frequency, forward and backward transitional probabilities, and all significant two-way interactions between these

21. Results Regression model includes… PCFGSRNboth Effect of surprisal according to… PCFG 0.45 p<.02 SRN Estimated β-coefficients (and associated p-values)

22. Results Regression model includes… PCFGSRNboth Effect of surprisal according to… PCFG 0.45 p<.02 SRN 0.64 p<.001 Estimated β-coefficients (and associated p-values)

23. Results Regression model includes… PCFGSRNboth Effect of surprisal according to… PCFG 0.45 p<.02 −0.46 p>.2 SRN 0.64 p<.001 1.02 p<.01 Estimated β-coefficients (and associated p-values)

24. Conclusions  Both PCFG and SRN do account for the reading-time data to some extent  But the PCFG does not improve on the SRN’s predictions No evidence for tree structures in the mental representation of sentences

25.  Why does the SRN fit the data better than the PCFG?  Is it more accurate on a particular group of data points, or does it perform better overall?  Take the regression analyses’ residuals (i.e., differences between predicted and measured reading times) δ i = |resid i ( PCFG )| − |resid i ( SRN )|  δ i is the extent to which data point i is predicted better by the SRN than by the PCFG.  Is there a group of data points for which δ is larger than might be expected?  Look at the distribution of the δs. Qualitative comparison

26. Possible distributions of δ 000 No difference between SRN and PCFG (only random noise) Overall better predictions by SRN Particular subset of the data is predicted better by SRN right-shifted right-skewed symmetrical, mean is 0

27.  If the distribution of δ is asymmetric, particular data points are predicted better by the SRN than the PCFG.  The distribution is not significantly asymmetric (two-sample Kolmogorov-Smirnov test, p>.17)  The SRN seems to be a more accurate psycholinguistic model overall. Test for symmetry

28. Questions I cannot (yet) answer (but would like to)  Why does the SRN fit the data better than the PCFG?  Perhaps people… −are bad at dealing with long-distance dependencies in a sentence? −store information about the frequency of multi-word sequences?

29. Questions I cannot (yet) answer (but would like to) In general:  Is this SRN a more accurate psychological model than this PCFG?  Are SRNs more accurate psychological models than PCFGs?  Does connectionism make for more accurate psychological models than grammar-based theories?  What kind of representations are used in human sentence processing? Surprisal-based model evaluation may provide some answers