Statistical Ranking Problem

Transcription

1 Statistical Ranking Problem Tong Zhang Statistics Department, Rutgers University

2 Ranking Problems Rank a set of items and display to users in corresponding order. Two issues: performance on top and dealing with large search space. web-page ranking rank pages for a query theoretical analysis with error criterion focusing on top machine translation rank possible (English) translations for a given input (Chinese) sentence algorithm handling large search space 1

3 Web-Search Problem User types a query, search engine returns a result page: selects from billions of pages. assign a score for each page, and return pages ranked by the scores. Quality of search engine: relevance (whether returned pages are on topic and authoritative) other issues presentation (diversity, perceived relevance, etc) personalization (predict user specific intention) coverage (size and quality of index). freshness (whether contents are timely). responsiveness (how quickly search engine responds to the query). 2

4 Relevance Ranking: Statistical Learning Formulation Training: randomly select queries q, and web-pages p for each query. use editorial judgment to assign relevance grade y(p, q). construct a feature x(p, q) for each query/page pair. learn scoring function ˆf(x(p, q)) to preserve the order of y(p, q) for each q. Deployment: query q comes in. return pages p 1,..., p m in descending order of ˆf(x(p, q)). 3

5 Measuring Ranking Quality Given scoring function ˆf, return ordered page-list p 1,..., p m for a query q. only the order information is important. should focus on the relevance of returned pages near the top. DCG (discounted cumulative gain) with decreasing weight c i DCG( ˆf, q) = m c i r(p i, q). j=1 c i : reflects effort (or likelihood) of user clicking on the i-th position. 4

6 Subset Ranking Model x X : feature (x(p, q) X ) S S: subset of X ({x 1,..., x m } = {x(p, q) : p} S) each subset corresponds to a fixed query q. assume each subset of size m for convenience: m is large. y: quality grade of each x X (y(p, q)). scoring function f : X S R. ranking function r f (S) = {j i }: ordering of S S based on scoring function f. quality: DCG(f, S) = m i=1 c ie yji (x ji,s) y ji. 5

7 Some Theoretical Questions Learnability: subset size m is huge: do we need many samples (rows) to learn. focusing quality on top. Learning method: regression. pair-wise learning? other methods? Limited goal to address here: can we learn ranking by using regression when m is large. massive data size (more than 20 billion) want to derive: error bounds independent of m. what are some feasible algorithms and statistical implications. 6

8 Bayes Optimal Scoring Given a set S S, for each x j S, we define the Bayes-scoring function as f B (x j, S) = E yj (x j,s) y j The optimal Bayes ranking function r fb that maximizes DCG induced by f B returns a rank list J = [j 1,..., j m ] in descending order of f B (x ji, S). not necessarily unique (depending on c j ) The function is subset dependent: require appropriate result set features. 7

9 Simple Regression Given subsets S i {y i,1,..., y i,m }. = {x i,1,..., x i,m } and corresponding relevance score We can estimate f B (x j, S) using regression in a family F: ˆf = arg min f F n i=1 m (f(x i,j, S i ) y i,j ) 2 j=1 Problem: m is massive (> 20 billion) computationally inefficient statistically slow convergence ranking error bounded by O( m) root-mean-squared-error. Solution: should emphasize estimation quality on top. 8

10 Importance Weighted Regression Some samples are more important than other samples (focus on top). A revised formulation: ˆf = arg minf F 1 n n i=1 L(f, S i, {y i,j } j ), with L(f, S, {y j } j ) = m j=1 w(x j, S)(f(x j, S) y j ) 2 + u sup j w (x j, S)(f(x j, S) δ(x j, S)) 2 + weight w: importance weighting focusing regression error on top zero for irrelevant pages weight w : large for irrelevant pages for which f(x j, S) should be less than threshold δ. importance weighting can be implemented through importance sampling. 9

11 Relationship of Regression and Ranking Let Q(f) = E S L(f, S), where L(f, S) = E {yj } j SL(f, S, {y j } j ) m = w(x j, S)E yj (x j,s) (f(x j, S) y j ) 2 + u sup w (x j, S)(f(x j, S) δ(x j, S)) 2 +. j j=1 Theorem 1. Assume that c i = 0 for all i > k. Under appropriate parameter choices with some constants u and γ, for all f: DCG(r B ) DCG(r f ) C(γ, u)(q(f) inf f Q(f )) 1/2. Key point: focus on relevant documents on top; j w(x j, S) is much smaller than m. 10

12 Generalization Performance with Square Regularization Consider scoring f ˆβ(x, S) = ˆβ T ψ(x, S), with feature vector ψ(x, S): L(β, S, {y j } j ) = ˆβ = arg min β H m j=1 [ 1 n ] n L(β, S i, {y i,j } j ) + λβ T β, (1) i=1 w(x j, S)(f β (x j, S) y j ) 2 + u sup j w (x j, S)(f β (x j, S) δ(x j, S)) 2 +. Theorem 2. Let M = sup x,s φ(x, S) 2 and W = sup S [ x j S w(x j, S) + u sup xj S w (x j, S)]. Let f ˆβ be the estimator defined in (1). Then we have DCG(r B ) E {Si,{y i,j } j } n i=1 DCG(r f ˆβ) C(γ, u) [ ( 1 + W M ) 2 inf (Q(f β) + λβ T β) inf 2λn β H f Q(f) ] 1/2. 11

13 Interpretation of Results Result does not depend on m, but the much smaller quantity quantity W = sup S [ x j S w(x j, S) + u sup xj S w (x j, S)] emphasize relevant samples on top: w is small for irrelevant documents. a refined analysis can replace sup over S by some p-norm over S. Can control generalization for the top portion of the rank-list even with large m. learning complexity does not depend on the majority of items near the bottom of the rank-list. the bottom items are usually easy to estimate. 12

14 Key Points Ranking quality near the top is most important statistical analysis to deal with the scenario Regression based algorithm to handle large search space importance weighting of regression terms error bounds independent of the massive web-size. 13

15 Statistical Translation and Algorithm Challenge Problem: conditioned on a source sentence in one language. generate a target sentence in another language. General approach: scoring function: measuring the quality of translated sentence based on the source sentence (similar to web-search) search strategy: effectively generate target sentence candidates. search for the optimal score. structure used in the scoring function (through block model). Main challenge: exponential growth of search space 14

16 Graphical illustration b 4 airspace Lebanese violate warplanes Israeli b 2 b 1 b 3 A l T A } r A t A l H r b y P A l A s r A } t n t h k A l m j A l A l j w y A l l b n A n y y l y P 15

17 Block Sequence Decoder Database: a set of possible translation blocks e.g. block a b translates into potential block z x y. Scoring function: candidate translation: a block sequence (b 1,..., b n ) map each sequence of blocks into a non-negative score s w (b n 1) = n i=1 wt F (b i 1, b i ; o). Input: source sentence. Translation: generate block sequence consistent with the source sentence. find the sequence with largest score. 16

18 Decoder Training Given source/target sentence pairs {(S i, T i )} i=1,...,n. Given a decoding scheme implementing: ẑ(w, S) = arg max z V (S) s w (z). Find parameter w such that on the training data: ẑ(w, S i ) achieves high BLEU score on average. Key difficulty: large search space (similar issues in MLR) Traditional tuning. Our methods: local training. global training. 17

19 Traditional scoring function A bunch of local statistics gathered from the training data, and beyond. different language models of the target P (T i T i 1, T i 2...). can depend on statistics beyond training data. Google benefited significantly from huge language models. orientation (swap) frequency. block frequency. block quality score: e.g. normalized in-block unigram translation probability: (S, T ) = ({s 1,..., s p }, {t 1,..., t q }); P (S T ) = j n j i p(s j t i ). Linear combination of log-frequencies (five or six features): s w ({b n 1}) = i j w j log f j (b i ; b i 1, ) Tuning: hand-tuning; gradient descent adjustment to optimize BLEU score 18

20 Large scale training Motivation: want: the ability of incorporating large number of features. require: automated training method to optimize millions of parameters. similar to the transition from inktomi rank function tuning to MLR. Challenges: search space V (S) is too large. Need to break it down. direct global training using relevant set model: treats decoder as a blackbox. 19

21 Global training of decoder parameter Treat decoder as a black box, implementing: ẑ(w, S) = arg max z V (S) s w (z). No need to know V (S). Generate truth set as block sequences with K-largest blue scores: V K (S). Generate alternatives as a subset of V (S) that are most relevant. if w does well on the relevant alternatives, then it does well on the whole set V (S). related to active sampling procedure in MLR. Some related works in parsing exist. 20

22 Learning method Try to minimize the following regularized empirical risk minimization: [ ] 1 N ŵ = arg min Φ(w, V K (S i ), V (S i )) + λw 2 w m i=1 Φ(w, V K, V ) = 1 K z V K max z V V K ψ(w, z, z ), ψ(w, z, z ) = φ(s w (z), Bl(z); s w (z ), Bl(z )), Relevant set: let ξ i (w, z) = max z V (S i ) V K (S i ) ψ(w, z, z ) V (r) (ŵ, S i ) = {z V (S i ) : z V K (S i ), ξ i (ŵ, z) 0 & ψ(ŵ, z, z ) = ξ i (ŵ, z)}. Key observation: can replace V by V (r) without changing solution. 21

23 Example loss functions Truth z V K, alternative z V V K (or in V (r) ). Bl(z) > Bl(z ): want to penalize if score s w (z) s w (z ). Estimate s w (z) using least squares (consistent): φ(s w (z), Bl(z); s w (z ), Bl(z )) = α(s w (z) Bl(z)) 2 + α (s w (z ) Bl(z )) 2. does not do well in our experiments. Possible reasons: we did implement correctly (re-weighting); s w (z) cannot approximate Bl(z) very well. Estimate s w (z) using pair-wise loss (inconsistent): φ(s w (z), Bl(z); s w (z ), Bl(z )) = (Bl(z) Bl(z )) max(1 (s w (z) s w (z )), 0) 2. 22

24 Approximate Relevant Set Method Observation: relevant set depends on w; w can be calculated based on approximate relevant set. Iterate to find both (similar to the active learning procedure in MLR). fix V (r), update w. fix w, update V (r). 23

25 Table 1: Generic Relevant Set Algorithm divide training points into m blocks J 1,..., J m initialize weight vector w w 0 for each data point S initialize truth V K (S) and alternative V (r) (S) for l = 1,, L for j = 1,, m for each S J j select relevant points { z k } S (*) update V (r) (S) V (r) (S) { z k } update w by solving the following approximately (**) N i=1 Φ(w, V K(S i ), V (r) (S i )) + λw 2 min w 1 m 24

26 Convergence analysis Approximate relevant set size: relevant set update rule (*): z k V (S) V k (S) for each z k V K (S) (k = 1,..., K) such that ψ(w, z k, z k ) = max ψ(w, z k, z ) z V (S) V K (S) convergence bound independent of the size of V (S). generalization bound: independent of size of V (S). real implementation using decoder output: z = max z V (S) s w (z ) Weight update rule in (**): can be implemented using stochastic gradient descent. 25

27 Experiments (MT03 Arabic-English DARPA evaluation) dj363.bleutest dl363.bleutest dj363.bleutraining dl363.bleutraining Figure 1: BLEU score on test and training data as a function of the number of training iterations. 26

28 Translation results (25 training iterations) Model training test MON-PHR MON SWAP Using only simple binary-features without language models, etc (never done before in SMT) MON-PHR: phrase id based features; MON/SWAP: plus internal word features with and without swapping. Context: many published grammar based methods, in the.20s. state of the art (with many additional engineering tuning): Google with huge language model (around.50), IBM (around.45). 27

29 Remarks Method to handle large search space for arbitrary decoding procedure key: restrict search space dependency through a loss function of the form max z V (S) A φ(z, ). z achieved at a small relevant set (can ignore the non-relevant points) using decoder output to approximate relevant set. automatic construction of most relevant alternatives. 28