Data Selection for Language Modeling Using Sparse Representations Abhinav Sethy, Tara N. Sainath, Bhuvana Ramabhadran, Dimitri Kanevsky IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A {asethy, tsainath, bhuvana, kanevsky}@ibm.us.com

Abstract The ability to adapt language models to specific domains from large generic text corpora is of considerable interest to the language modeling community. One of the key challenges is to identify the text material relevant to a domain in the generic text collection. The text selection problem can be cast in a semi-supervised learning framework where the initial hypothesis from a speech recognition system is used to identify relevant training material. We present a novel sparse representation formulation which selects a sparse set of relevant sentences from the training data which match the test set distribution. In this formulation, the training sentences are treated as the columns of the sparse representation matrix and the n-gram counts as the rows. The target vector is the n-gram probability distribution for the test data. A sparse solution to this problem formulation identifies a few columns which can best represent the target test vector, thus identifying the relevant set of sentences from the training data. Rescoring results with the language model built from the data selected using the proposed method yields modest gains on the English broadcast news RT-04 task, reducing the word error rate from 14.6% to 14.4%. Index Terms: language modeling, data selection

1. Introduction A common component of many statistical Natural Language Processing (NLP) systems that can benefit from the use of large text corpora like the web is the n-gram language model. In the context of Automatic Speech Recognition (ASR), the n-gram model is used as a prior for decoding the acoustic sequence. The n-gram model is trained from counts of word sequences seen in a corpus and hence its quality depends on the amount of training data as well as the degree to which the training statistics represent the target application. Text harvested from the web and other large text collections such as the English Gigaword corpus provide a good resource to supplement the in-domain data for a variety of applications [1, 2]. However even with the best queries and text collection schemes, both the style and content of the data acquired tend to differ significantly from the specific nature of the domain of interest. In order to maximize the benefit from building language models from these generic corpora, we need to identify subsets of text relevant to the target application. In most cases we have a set of in-domain example sentences available to us which can be used in a semi-supervised fashion to identify the text relevant to the application of interest. The dominant theme in recent research literature for achieving this is the use of various rank-and-select schemes for identifying sentences from the large generic collection which match the indomain data [1, 2]. An alternative to using the in-domain data to identify the relevant text training material is to use the first pass ASR output of the test set to select data from the auxil-

iary out of domain sources. The selected data can then be used for adapting the in-domain language model and the test set can be re-decoded with the adapted language model in a two pass approach. This approach can be seen as a way to correct the implicit assumption that the test set is drawn from the same distribution as the training set [3]. The common approach to data selection in literature is to rank order the available data using a similarity criterion using a model built from the in-domain set and then select top sentences. Rank-and-select filtering schemes select individual sentences on the merit of their match to the in-domain model. As a result, even though individual sentences might be good indomain examples, the overall distribution of the selected set is imbalanced with a bias towards the high probability regions of the distribution. An alternative to rank based selection is subset selection based on distributional similarity [4] where a incremental relative entropy (r.e) criterion is used to select a subset of sentences. In this paper, we present a new sparse representation formulation for language model data selection which identifies a minimal set of sentences that match the target distribution, i.e the distribution of the in-domain data. In this formulation the out-of-domain sentences are treated as the columns of the sensing matrix with the word counts corresponding to the n-grams being the rows. The target vector is the n-gram probability distribution of the in-domain data. By identifying a sparse solution to this regression problem we aim to identify a relevant subset of sentences from out-of-domain data that can best represent the target vector. We believe that using the sparse representation formulation will help identify an unbiased matching subset similar to the relative entropy criterion [4]. In addition, the sparse representation solution provides a global solution for identifying the important examples from the training set and does not require an incremental greedy approach [4] with the need for multiple randomized solutions. In the next section (Section 2) we review existing methods for data selection for language modeling. We then describe the proposed sparse reconstruction based data selection approach in Section 3. Section 4 describes out experimental setup and results on an English broadcast news task. We conclude with a brief review of the key findings of this paper.

2. Related Work The central theme in recent work on data selection schemes for using large generic corpora in domain specific tasks to build language models, has been to use a scoring function that measures the similarity of each observed sentence in the corpus to the domain of interest (in-domain) and assign an appropriate score. The subsequent step is to set a threshold in terms of this score or the number of top scoring sentences, usually done on a heldout data set, and use this threshold as a criterion in the data selection

process. A common choice for a scoring function is in-domain model perplexity [1, 5] and variants involving comparison to a generic language model [6]. A modified version of the BLEU metric which measures sentence similarity in machine translation has been proposed by Sarikaya [2] as a scoring function. The ranking problem can also be cast into a classification problem by using methods which Learn from Positive and Unlabeled examples (LPU) [7]. In LPU, a binary classifier is trained using a subset of the unlabeled set as the negative or noise set and the in-domain data as the positive set. The binary classifier is then used to relabel the sentences in the corpus. The classifier can then be iteratively refined by using a better and larger subset of the sentences labeled in each iteration. In [4] a relative entropy based subset selection scheme was proposed which tries to optimize the selection of the set as a whole in contrast to sentence selection by ranking. Adaptive topic models such as [8] [9] where the topic distribution is adapted to the test set can also be seen as a way to select relevant data in the model space. In the broader context of statistical learning, the problem of selecting relevant data is akin to the classical problem of sample selection bias [3]. Resampling of train data for matching test and train distribution and correcting sample selection bias was used in [10] for better discriminative training of a maximum entropy classifier. In [11], resampling is used to select relevant auxiliary data for improving the language model for Machine Translation.

3. Data Selection using Sparse Representations In this section, we discuss the use of sparse representations for data selection. 3.1. Sparse Representation The problem of selecting a representative set of sentences can be cast as a sparse representation problem by using a vector representation of the test set as the target value of the regression model and using training samples as the input vectors. Solving for the optimal regression coefficients under sparsity constraints can thus identify the most relevant set of sentences. This approach has been used in [12] for phonetic classification, and in this paper we extend its use for data selection. We formulate the sparse representation methodology for data selection as follows. First, a dictionary H is constructed consisting of possible training samples, that is H = [h1 ; h2 . . . ; hn ] ∈ ℜm×n . Given the test feature vector y ∈ ℜm , the goal of sparse representations is to solve the problem y = Hβ subject to a sparseness constraint on β. This sparseness constraint on β acts as a regularization term to prevent over-fitting and reduce sensitivity to outliers. Sparse representations require that the dimension of each feature vector in H (i.e. m) is less than number of training examples n. Thus H is an over-complete dictionary whereby β selects a few relevant examples from H to represent y. For the case of two pass LM rescoring, we use normalized word counts from the first pass output of the ASR system to construct the target vector y. The word counts of training sentences are similarly normalized to form the columns of the sensing matrix H with each hi column representing the normalized counts for a training sentence. Various sparse representation methods can be used to solve the y = Hβ problem. In this paper, we solve for β using the Approximate Bayesian Compressive Sensing (ABCS) method

[13], which imposes a combination of a semi-Gaussian and l2 norm on β. Thus, we can formulate the sparse representation problem using ABCS in Equation 1, where ∥ β ∥21 < ϵ represents the semi-Gaussian constraint. y = Hβ s.t.

∥ β ∥21 < ϵ for β

(1)

The solution to Equation 1 produces a weight vector β, whose elements reflect the importance of the corresponding training data samples (i.e. columns of H). In the next step we select sentences with high value of β as the representative set. More details on this selection methodology will be discussed in Section 4. 3.2. Implementation Details Sparse representation algorithms make it possible to use an l1 approximation to solve the in-tractable l0 problem of subset solution. However, they still require significant computational resources in terms of both CPU time and memory. Clustering the training data helps to reduce the computation cost significantly by reducing the number of columns in the sensing matrix H. We experimented with two approaches to cluster the training data. In the first approach we simply merge a fixed number of consecutive sentences into one cluster. This approach assumes that there is some semantic or topical consistency in sentences that occur next to each other. In the second approach we use a statistical document clustering algorithm to identify similar sentences and merge them together using a term frequencyinverse document frequency (TF-IDF) vector representation of sentences with bisecting k-means [14]. For both the sentence merging and k-means approaches a cluster is treated as a column in the H matrix by merging the word counts of all the sentences that form the cluster. Thus the number of columns is reduced from the number of training sentences to the number of clusters. The vocabulary used to build the vector representation of the training and test sets is another interesting design parameter for the data selection problem. A large vocabulary provides full coverage of the test and training data but can lead to a non robust estimate because many of the counts will be very low or close to zero. We experimented with using words with high topic specificity as the vocabulary (TopicVoc) to better constrain the data selection problem. In order to identify these words we first cluster the training data in an unsupervised fashion using a fast bisecting k-means algorithms and then identify words with high mutual information to class labels [14]. We also experimented with restricting the vocabulary to words which are frequently erroneously recognized by the ASR ( ErrVoc). For finding these words we use the word confusion matrix from a decode of the development set. The first pass decode of the test set can be used in multiple ways to build the target vector y. We can build the vector y using either the 1-best first pass output or use posterior weighted counts of words in lattices, confusion networks or N-best lists. In our experiments we will present results with both 1-best output counts and fractional counts. Using a single target vector built from the entire data assumes that the test set is drawn from one distribution or topic. In the absence of meta-data information we can infer topics on the test set using an unsupervised clustering algorithm. As we will show in our results in Section 4, using multiple y vectors corresponding to automatically inferred topics can help us acquire better data.

4. Experiments and Results The LVCSR system is based on the 2007 IBM Speech transcription system for GALE Distillation Go/No-go Evaluation [15]. The acoustic models are discriminatively trained on speakeradapted perceptual linear predictive (PLP) features. These acoustic models are used across all the experiments presented in this paper. We used the 1996 CSR Hub4 Language Model data (LDC98T31) for building the models in our experiments. Kneser-Ney smoothing was used for building the baseline 4gram language model. The size of the training text was 140M words and the baseline model has 40M n-grams. The recognition vocabulary has 84K word tokens with an average of 1.08 pronunciation variants per word. Where possible, pronunciations are based on the PRONLEX dictionary. We report Word Error Rate (WER) results on the RT-04 task (40K words) while the perplexity results are on the RT-04 Development set (15K words). The baseline language model had a perplexity of 204 on the RT-04 Dev set (Dev-04). This baseline system which had a WER of 14.6% was used to produce confusion networks and lattices for our experiments. The consensus networks from the baseline system were used to create the target count vector y for our data selection process (Section 3). For each word wi we calculate the expected count sum as follows: c(i) =



WER. This is in agreement the results in [16] where no significant improvement was seen over unigram relative entropy based data selection. This will be investigated in our future work on data selection.

Figure 1: Fraction of data selected versus relative entropy between unigram language models from test set and selected data with full vocabulary.

(pj (i))

j∈S(i)

where S(i) is the set of consensus bins where the word wi is seen and pj (i) is the posterior probability of word i in bin j. The ∑ counts c(i) are then normalized by the total count C = i c(i) to generate the target vector y. The target vector can also be generated using counts from the 1-best ASR output. The sparse representation based data selection formulation that we discussed in Section 3 tries to find a sparse set of training exemplars which can serve as a basis to fit the target count vector from the test set. The goodness of the β solution is measured in terms of the residual error between y and Hβ, that is ||y − Hβ||2 . We first evaluate whether sentences corresponding to high β values indeed match in distribution to the in-domain set. To do this we rank the sentences according to their β values and select the top r% sentences. We then compare the relative entropy between the test set and the selected sentences for different values of r. The case of r = 100 corresponds to not doing any data selection. The results are shown in Figure 1. As can be seen from the figure the selected data has lower entropy than the entropy of all the data (rightmost point on the graph). In order to rescore the ASR output using the selected sentences, we build a Kneser-Ney smoothed 4-gram language model from the selected data which is merged with the baseline language model. The merged language model is then used to rescore the ASR lattices. The merge weight is optimized to minimize the WER on the RT-04 development set (Dev-04). The optimal weight was found to be 0.3 though there was no change in performance if the weight was varied between a range of 0.1 to 0.5. Figure 2 shows the WER for different percentages of data selected with the full test set vocabulary being used for representing the sentence vectors. The fraction of data selected for optimal WER points (15% and 20%) can be related to the point after which the unigram relative entropy between test set and selected data starts to increase (Figure 1). We also experimented with using higher order n-gram’s as features for data selection but did not observe any improvement in perplexity or

Figure 2: Fraction of data selected versus test set WER with full vocabulary Table 1 shows the results on RT-04 with different vocabularies for building the train and test vectors as described in Section 3. The first row corresponds to using the full test set vocabulary, the second row corresponds to using words with high mutual information with automatically assigned class labels and the third set corresponds to using words with high error rate identified on the development set. Selecting 15% of the data corresponding to the minimum perplexity point in Figure 2 using the full test vocabulary (6K words) did not give any WER improvement. However, when we used a vocabulary subset comprising of high mutual information (TopicVoc) or error rate (ErrVoc) words the WER reduced to 14.5%. The results in Table 1 correspond to merging consecutive sentences in the training data into clusters. We also experimented with unsupervised clustering of the training data (Section 3) but we did not see any improvement. This indicates that our assumption that consecutive sentences tend to belong to the same topic seems to hold for at least the broadcast news corpus. Next we experimented with unsupervised k-means clustering of test set utterances to identify utterances which belonged to the same topic. We varied the number of topics from 5 to 20. Data selection and rescoring are carried out for each cluster/topic separately. The best result of 14.4% was obtained us-

Methodology Full test set vocabulary Top 2K words ordered by information gain (TopicVoc) Top 2K words ordered by error rate (ErrVoc) Topic based data selection (5 topics, TopicVoc)

WER 14.6 14.5 14.5 14.4

Table 1: WER with different vocabularies and topic clustering mechanisms

ing 5 topics and a vocabulary of 2K words from the test set which had high mutual information with the class labels. The fraction of data selected for all the topics was the same (15%). It is possible to improve the performance slightly (0.1%) by tuning the percentage of data selected for individual topics on the test set. However, we cannot use the held-out set to fix the data selected for individual topics since the topics are identified in an unsupervised fashion and there is no direct correspondence between the topics identified on the development set and the test set.

5. Conclusion In this paper we explored a sparse representation based approach for selecting relevant language modeling data using the first pass output of the ASR system. Both the test and training text data are represented as bag-of-word vectors. The data selection problem is formulated as a constrained linear regression problem where the target is the test vector from the first pass ASR output and the input samples are vectors from the training text. The weight vector obtained by solving this linear regression problem is then used to identify the relevant sentences. Our experiments show that using a vocabulary consisting of words with high information gain or words with high error rate for the vector representation performs better than using the full vocabulary. Using unsupervised methods to cluster the test data into topics and identifying relevant data for each topic provides additional gains. Using the proposed method we were able to achieve a modest improvement of 0.2% (14.4% WER) on a baseline of 14.6% for the English RT-04 task. Although the best results in this paper were obtained with word or unigram counts, the proposed approach does support other features such as higher order n-gram features as part of the vector representation for both training and test sentences. The question of which n-gram features are most useful for data selection will be explored in our future work on this technique. We would also like to explore methods to use the sparse reconstruction based data selection ideas presented in this paper as a means to prune n-gram language models.

6. References [1] T. Ng, M. Ostendorf, M.-Y. Hwang, M. Siu, I. Bulyko, and X. Lei, “Web-data augmented language model for Mandarin speech recognition,” in Proceedings of ICASSP, 2005. [2] R. Sarikaya, A. Gravano, and Y. Gao, “Rapid language model development using external resources for new spoken dialog domains,” in Proceedings of ICASSP, 2005. [3] J. Heckman, “Sample selection bias as a specification error,” Econometrica, 1979. [4] A. Sethy, P. G. Georgiou, and S. Narayanan, “Text data

acquisition for domain-specific language models,” in Proceedings of EMNLP, 2006. [5] T. Misu and T. Kawahara, “A bootstrapping approach for developing language model of new spoken dialogue systems by selecting web texts,” in Proceedings of ICSLP, 2006. [6] K. Weilhammer, M. N. Stuttlem, and S. Young, “Bootstrapping language models for dialogue systems,” in Proceedings of ICSLP, 2006. [7] B. Liu, Y. Dai, X. Li, W. S. Lee, and P. Yu, “Building text classifiers using positive and unlabeled examples,” in Proceedings of ICDM, 2003. [8] Y. C. Tam and T. Schultz, “Correlated latent semantic model for unsupervised lm adaptation,” in Proceedings of ICASSP, 2007. [9] J. Bellegarda, “Statistical language model adaptation: review and perspectives,” Speech Communication, pp. 93– 108, 2004. [10] S. Bickel, M. Brckner, , and T. Scheffer, “Discriminative learning for differing training and test distributions,” in Proceedings of ICML, 2007. [11] S. Maskey and A. Sethy, “Resampling auxiliary data for language model adaptation in machine translation for speech,” in Proceedings of ICASSP, 2009. [12] T. N. Sainath, A. Carmi, D. Kanevsky, and B. Ramabhadran, “Bayesian Compressive Sensing for Phonetic Classification,” in Proceedings of ICASSP, 2010. [13] A. Carmi, P. Gurfil, D. Kanevsky, and B. Ramabhadran, “ABCS: Approximate Bayesian Compressed Sensing,” Human Language Technologies, IBM, Tech. Rep., 2009. [14] S. Hahn, A. Sethy, H.-K. J. Kuo, and B. Ramabhadran., “study of unsupervised clustering techniques for language modeling,” in Proceedings of Interspeech, 2008. [15] S. Chen, B. Kingsbury, L. Mangu, D. Povey, G. Saon, H. Soltau, and G. Zweig, “Advances in speech transcription at IBM under the DARPA EARS program,” IEEE Transactions on Audio, Speech and Language Processing, pp. 1596–1608, 2006. [16] A. Sethy and S. N. Panayiotis G. Georgiou, Bhuvana Ramabhadran, “An iterative relative entropy minimization-based data selection approach for n-gram model adaptation,” IEEE transactions on audio, speech, and language processing, 2009.

Data Selection for Language Modeling Using Sparse ...

semi-supervised learning framework where the initial hypothe- sis from a ... text corpora like the web is the n-gram language model. In the ... represent the target application. ... of sentences from out-of-domain data that can best represent.

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