acl acl2012 acl2012-34 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Sam Sahakian ; Benjamin Snyder
Abstract: We propose a new approach for the creation of child language development metrics. A set of linguistic features is computed on child speech samples and used as input in two age prediction experiments. In the first experiment, we learn a child-specific metric and predicts the ages at which speech samples were produced. We then learn a more general developmental index by applying our method across children, predicting relative temporal orderings of speech samples. In both cases we compare our results with established measures of language development, showing improvements in age prediction performance.
Reference: text
sentIndex sentText sentNum sentScore
1 edu Abstract We propose a new approach for the creation of child language development metrics. [sent-3, score-0.539]
2 A set of linguistic features is computed on child speech samples and used as input in two age prediction experiments. [sent-4, score-0.851]
3 In the first experiment, we learn a child-specific metric and predicts the ages at which speech samples were produced. [sent-5, score-0.407]
4 We then learn a more general developmental index by applying our method across children, predicting relative temporal orderings of speech samples. [sent-6, score-0.718]
5 In both cases we compare our results with established measures of language development, showing improvements in age prediction performance. [sent-7, score-0.301]
6 1 Introduction The rapid childhood development from a seem- ingly blank slate to language mastery is a puzzle that linguists and psychologists continue to ponder. [sent-8, score-0.271]
7 While the precise mechanism of language learning remains poorly understood, researchers have developed measures of developmental language progress using child speech patterns. [sent-9, score-0.972]
8 These metrics provide a means of diagnosing early language disorders. [sent-10, score-0.148]
9 Besides this practical benefit, precisely measuring grammatical development is a step towards understanding the underlying language learning process. [sent-11, score-0.11]
10 Previous NLP work has sought to automate the calculation of handcrafted developmental metrics proposed by psychologists and linguists. [sent-12, score-0.653]
11 If so, how well would such a measure generalize across children? [sent-16, score-0.086]
12 This last question touches on an underlying assumption made in much of the child language literature– that while children progress grammatically at different rates, they follow fixed stages in their development. [sent-17, score-0.682]
13 If a developmental index automatically learned from one set of children could be accurately applied to others, it would vindicate this assumption of shared developmental paths. [sent-18, score-1.0]
14 Several metrics of language development have been set forth in the psycholinguistics literature. [sent-19, score-0.286]
15 , 2006)– a score for individual sentences based on the observed presence of key syntactic structures. [sent-22, score-0.08]
16 Today, these hand-crafted metrics persist as measurements of child language development, each taking a slightly different angle to assess the same question: Exactly how much grammatical knowledge does a young learner possess? [sent-23, score-0.598]
17 NLP technology has been applied to help automate the otherwise tedious calculation of these measures. [sent-24, score-0.113]
18 In response to its limited depth of analysis and the necessity for human supervision in CP, there have since Proce Jedijung, sR oefpu thbeli c50 othf K Aonrneua,a8l -M14e Jtiunlgy o 2f0 t1h2e. [sent-26, score-0.054]
19 In this and all following tables, traditional developmental metrics are shaded. [sent-29, score-0.509]
20 Likewise, in the ESL domain, Chen and Zechner (201 1) automate the evaluation of syntactic complexity of non-native speech. [sent-32, score-0.151]
21 However, the definition of first-language developmental metrics has as yet been left up to human reasoning. [sent-34, score-0.475]
22 In this paper, we consider the automatic induction of more accurate developmental metrics using child language data. [sent-35, score-0.934]
23 We extract features from longitudinal child language data and conduct two sets of experiments. [sent-36, score-0.543]
24 For individual children, we use least-squares regression over our features to predict the age of a held-out language sample. [sent-37, score-0.378]
25 We find that on average, existing single metrics of development are outperformed by a weighted combination of our features. [sent-38, score-0.189]
26 In our second set of experiments, we investigate whether metrics can be learned across children. [sent-39, score-0.208]
27 To do so, we consider a speech sample ordering task. [sent-40, score-0.12]
28 We use optimization techniques to learn weight- ings over features that allow generalization across children. [sent-41, score-0.189]
29 Although traditional measures like MLU and D-level perform well on this task, we find that a learned combination of features outperforms any single pre-defined developmental score. [sent-42, score-0.557]
30 2 Data To identify trends in child language learning we need a corpus of child speech samples, which we 96 9,000 Utcreants264,7250 0142 8354295637PRA NS0 eobdiantsearo asm rh7 i Age (months) Figure 1: Number of utterances across ages of each child in our corpus. [sent-43, score-1.644]
31 CHILDES is a collection of corpora from many studies of child language based on episodic speech data. [sent-47, score-0.514]
32 Since we are interested in development over time, our corpus consists of seven longitudinal studies of individual children. [sent-48, score-0.177]
33 Data for each child is grouped and sorted by the child’s age in months, so that we have a single data point for each month in which a child was observed. [sent-49, score-1.127]
34 , 2007) and harvest features that should be informative and complementary in assessing grammatical knowledge. [sent-52, score-0.066]
35 Beyond the three traditional developmental metrics, we record five additional features. [sent-54, score-0.4]
36 We count two of Brown’s (1973) obligatory morphemes articles and contracted auxiliary “be” verbs as well as occurrences of any preposition. [sent-55, score-0.165]
37 These counted features are normalized by a child’s total number of utterances at a given age. [sent-56, score-0.105]
38 Finally, we include two vocabulary-centric features: Average word fre— — tion of linear regression on individual children. [sent-57, score-0.133]
39 The lowest error for each child is shown in bold. [sent-58, score-0.488]
40 how often a word is used in a stan- dard corpus) as indicated by CELEX (Baayen et al. [sent-61, score-0.028]
41 , 1995), and the child’s ratio of function words (determiners, pronouns, prepositions, auxiliaries and conjunctions) to content words. [sent-62, score-0.028]
42 To validate a developmental measure, we rely on the assumption that a perfect metric should increase monotonically over time. [sent-63, score-0.429]
43 We therefore calculate Kendall’s Tau coefficient (τ) between an ordering of each child’s speech samples by age, and an ordering by the given scoring metric. [sent-64, score-0.341]
44 The τ coefficient is a measure of rank correlation where two identical orderings receive a τ of 1, complete opposite orderings receive a τ of -1, and independent orderings are expected to receive a τ of zero. [sent-65, score-0.663]
45 The τ coefficients for each of our 8 features individually applied to the 7 children are shown in Table 1. [sent-66, score-0.193]
46 We note that the pre-defined indices of language development MLU, tree depth and D-Level perform the ordering task most accurately. [sent-67, score-0.199]
47 To illustrate the degree of variance between children and features, we also include plots of each child’s DLevel and contracted auxiliary “be” usage in Figure 2. [sent-68, score-0.318]
48 — — 3 Experiments Learning Individual Child Metrics Our first task is to predict the age at which a held-out speech sample was produced, given a set of age-stamped samples from the same child. [sent-69, score-0.356]
49 We perform a least squares regression on each child, treating age as the dependent variable, and our features as independent variables. [sent-70, score-0.329]
50 Each data set is split into 10 random folds of 90% training and 10% test data. [sent-71, score-0.04]
51 8 /0 Content Table 3: Average τ of orderings produced by MLU (the best traditional index) and our learned metric, versus true chronological order. [sent-77, score-0.276]
52 achieves lower error than any individual feature by itself. [sent-79, score-0.078]
53 Learning General Metrics Across Children To produce a universal metric of language development like MLU or D-Level, we train on data pooled across many children. [sent-80, score-0.182]
54 For each of 7 folds, a single child’s data is separated as a test set while the remaining children are used for training. [sent-81, score-0.157]
55 Since Ross is the only child with samples beyond 62 months, we do not attempt to learn a general measure of language development at these ages, but rather remove these data points. [sent-82, score-0.731]
56 Unlike the individual-child case, we do not predict absolute ages based on speech samples, as each child is expected to learn at a different rate. [sent-83, score-0.711]
57 Instead, we learn an ordering model which attempts to place each sample in its relative place in time. [sent-84, score-0.118]
58 The model computes a score from a weighted quadratic combination of our features and orders the samples based on their computed scores. [sent-85, score-0.168]
59 To learn the parameters of the model, we seek to maximize the Kendall τ between true and predicted orderings, summed over the training children. [sent-86, score-0.053]
60 We pass this objective function to Nelder-Mead (Nelder and Mead, 1965), a standard gradient-free optimization algorithm. [sent-87, score-0.061]
61 NelderMead constructs a simplex at its initial guess of parameter values and iteratively makes small shifts in the simplex to satisfy a descent condition until a local maximum is reached. [sent-88, score-0.154]
62 We report the average Kendall τ achieved by this algorithm over several feature combinations in Table 3. [sent-89, score-0.032]
63 Because we modify our data set in this experiment, for comparison we also show the average Kendall τ achieved by MLU on the truncated data. [sent-90, score-0.032]
64 4 Discussion Our first set of experiments verified that we can achieve a decrease in mean squared error over existing metrics in a child-specific age prediction task. [sent-91, score-0.441]
65 Figure 2: Child age plotted against D-Level (top) and counts of contracted auxiliary “be” (bottom) with best fit lines. [sent-106, score-0.421]
66 Since our regression predicts child age, age in months is plotted on the y-axis. [sent-107, score-0.868]
67 in favor of the learned metric by the apparent difficulty of predicting Ross’s age. [sent-108, score-0.123]
68 As demonstrated in Figure 2, Ross’s data exhibits major variance, and also includes data from later ages than that of the other children. [sent-109, score-0.144]
69 It is well known that MLU’s per- formance as a measure of linguistic ability quickly drops off with age. [sent-110, score-0.047]
70 During our first experiment, we also attempted to capture more nuanced learning curves than the linear case. [sent-111, score-0.035]
71 Specifically, we anticipated that learning over time should follow an S-shaped curve. [sent-112, score-0.028]
72 This follows from observations of a “fast mapping” spurt in child word learning (Woodward et al. [sent-113, score-0.459]
73 , 1994), and the idea that learning must eventually level off as mastery is attained. [sent-114, score-0.065]
74 To allow our model to capture non-linear learning rates, we fit logit and quadratic functions to the data. [sent-115, score-0.077]
75 With every other child, these functions fit the data to a linear section of the curve and yielded much larger errors than simple linear regression. [sent-117, score-0.037]
76 In our second set of experiments, we attempted to learn a general metric across children. [sent-120, score-0.19]
77 Here we also achieved positive results with simple methods, just edging out established measures of language de- velopment. [sent-121, score-0.092]
78 The generality of our learned metric supports the hypothesis that children follow similar paths of language development. [sent-122, score-0.28]
79 Although our learned solution is slightly more favorable than preexisting metrics, it performs very little learning. [sent-123, score-0.06]
80 Using all features, learned parameter weights remain at or extremely close to the starting point of 1. [sent-124, score-0.06]
81 98 Through trial and error, we discovered we could improve performance by omitting certain features. [sent-125, score-0.031]
82 In Table 3, we report the best discovered feature combination including only two relatively uncorrelated features, MLU and function/content word ratio. [sent-126, score-0.031]
83 If downweighting some features yields a better result, we would expect to discover that with our optimization algorithm, but this evidently not the case, perhaps due to our limited sample of 7 children. [sent-127, score-0.097]
84 The fact that weights move so little suggests that our best result is stuck in a local maximum. [sent-128, score-0.056]
85 To investigate this, we also experimented with Differential Evolution (Storn and Price, 1997) and SVMranking (Joachims, 2002), the former a global optimization technique, and the latter a method de- veloped specifically to learn orderings. [sent-129, score-0.114]
86 Although these algorithms are more willing to adjust parameter weights and theoretically should not get stuck in local maxima, they are still edged out in performance by Nelder-Mead. [sent-130, score-0.056]
87 It may be that the early stopping of Nelder-Mead serves as a sort of smoothing in this very small data-set of 7 children. [sent-131, score-0.039]
88 Our improvements over hand-crafted measures of language development show promise. [sent-132, score-0.141]
89 In the case of individual children, we outperform existing measures of development, especially past the early stages of development when MLU ceases to correlate with age. [sent-133, score-0.292]
90 Our attempts to learn a metric across children met with more limited success. [sent-134, score-0.312]
91 However, when we restricted our regression to two of the least correlated features, MLU and the function/content word ratio, we were able to beat manually created metrics. [sent-135, score-0.084]
92 These results suggest that more sophisticated models and techniques combined with more data could lead to more accurate metrics as well as insights into the language learning process. [sent-136, score-0.109]
93 Computing and evaluating syntactic complexity features for automated scoring of spontaneous non-native speech. [sent-173, score-0.133]
94 Automatic measurement of syntactic complexity in child language acquisition. [sent-209, score-0.567]
95 Indicators of linguistic competence in the peer group conversational behavior of mildly retarded adults. [sent-227, score-0.08]
96 Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. [sent-262, score-0.061]
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