A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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Checkpoint Instances Google PT Our system Noun 4790 0.313 72 0.351 29 0.418 57 Verb 953 0.211 94 0.307 69 0.283 65 Det_Adj_N 278 0.379 28 0.445 72 0.480 17 Dass_Pron_Verb 20 0.375 37 0.383 56 0.383 56 Verb_Pron_DetNoun 17 0.224 97 0.244 09 0.409 45 Weil_Pron_Verb 10 0.236 26 0.311 11 0.577 78 Pass 7 0.134 50 0 1 Table 2: Evaluation results for German-French 5 Evaluation Experiments In this experiment, we compare our in-house SMT system with 2 other systems, Google Translate 8 and Personal Translator (PT) 9 , in terms of handling specific linguistic checkpoints. Our SMT system was trained according to the instructions for building a baseline system at WMT 2011 10 , with the difference that we use MGIZA++ [4] for computing the alignments. As training data we use Alpine texts from the Text+Berg corpus (approx. 200000 sentence pairs German-French). The test corpus comprises 1000 sentence pairs from our Alpine treebank. For all systems, we use the manually-checked alignments extracted from the treebank. The comparison will be based on checkpoints which we considered particularly interesting for each translation direction, most of them PoS-based. Table 2 contains the evaluation results for the language pair German-French. We have investigated the following checkpoints: nouns, finite verbs, noun phrases consisting of a determiner, an adjective and a noun (Det_Adj_N), subordinate clauses introduced by dass (EN: that) and weil (EN: because) and verb-subjectobject collocations (Verb_Pron_DetNoun). Additionally, we have also considered the ambiguous word Pass (EN: passport, mountain pass, amble). One notices that Personal Translator usually performs better than Google, probably because, being a rule-based system, it is aware of grammatical constructions and knows how to handle them properly. Its weaknesses are mostly related to the choice of words and unknown words, respectively. Since we are now looking at particular grammatical structures, it is likely for a rule-based system to analyze them adequately. Another evidence for this claim is the fact that Personal Translator outperforms all the other systems with respect to finite verbs, which pose difficulties in German (e. g. separable verbs). Our in-house MT system performs in all cases better than its opponents because it has been trained with texts from the same domain. It thus gains strongly in vocabulary coverage. The most striking example is the German word Pass (EN: 8 http://translate.google.com 9 http://www.linguatec.net/products/tr/pt 10 http://www.statmt.org/wmt11/baseline.html 152
Checkpoint Instances Google PT Our system Noun_de_Noun 346 0.225 09 0.183 07 0.415 81 Poss_Noun 289 0.292 73 0.390 96 0.469 55 que_V 224 0.292 99 0.292 99 0.340 76 que_Pr_V 115 0.390 41 0.397 26 0.445 21 Ne_V_Pas 45 0.447 37 0.381 58 0.526 32 Verb_Pr_Vinf 25 0.158 73 0.301 59 0.396 83 V_Vinf_DetN 23 0.133 93 0.294 64 0.250 00 Table 3: Evaluation results for French-German mountain pass), translated as col in the mountaineering domain, but as either passeport (EN: passport) or la passe (EN: pass [the ball]) in other contexts. The analysis of the words with lemma Pass is reproduced in the last line of table 2. We notice that Personal Translator could not reproduce the correct meaning of the word in this context. Google succeeded getting one checkpoint instance right due to the collocation with a proper noun. As a result, it could correctly translate Forcola-Pass as col Forcola. For the opposite translation direction, we have chosen linguistic structures particular for the source language (French): nominal phrases consisting of two nouns separated by the preposition de (such as in journée d’été) or of a possessive adjective and a noun (such as in notre voisin). Besides, we have selected relative clauses introduced by the word que, which can be interpreted as either a relative pronoun or a conjunction. Finally, we have considered verbal constructions: the negative form of verbs (e.g. ne résisterait pas), modal constructions involving nouns (e.g. doit affronter le parcours) and pronouns (e.g. peut nous aider), respectively. The results are presented in table 3. The best-handled construction by all three systems is the negative verbal form, followed by the relative clause introduced by que and followed by a pronoun. If we remove the restriction that que is directly followed by a pronoun, the particle becomes ambiguous and this causes the drop of 10% between the scores in the third and the fourth line. Noun phrases are also handled well by our system, whereas complex verbal phrases raise challenges. The rule-based system Personal Translator gets the best score in the latter case due to its linguistic knowledge background, as we have noticed before. 5.1 Limitations of the System As DELiC4MT evaluation uses string-based comparisons, it penalizes every small difference from the reference text. Consequently it will equally penalize a MT system for dropping a word as for misspelling a single letter from a word. This is particularly disadvantageous for SMT systems, which use no linguistic information (grammar, syntax or semantics). On the other hand, some of the limitations of 153
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A Treebank-based Investigation of I
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participle and a (te-)infinitival c
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Some verbs occur twice, since they
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Profiling Feature Selection for Nam
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prepositional objects (FOPP, OPP).
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the limited size of annotated data
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with high precision typically have
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‘This was “not significantly”
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The preposition durch typically has
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Non-Projective Structures in Indian
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the sequential order of nodes in a
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extra-posed relative clause that ge
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Experiments on Dependency Parsing o
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for mitigating the effects of spars
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obtained with MALTParser is 76.61%
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as a standardised serialisation for
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constituency and dependency, possib
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SynAF and/or in . However, they sha
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shows how some elements that are no
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Example
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‏ Example 8: represent
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Chinese) as the direct object NP.
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Example 15: Tokens and Word Forms
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In a second experiment, a dataset w
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[3] Leech G. N., Barnett, R. & Kahr
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Effectively long-distance dependenc
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In French, another case of eLDD is
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elativization, it-clefts or questio
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4.2.3 Annotation methodology Becaus
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Number of occurrences in FTB +SEQTB
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producing treebank based LFG approx
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Logical Form Representation for Lin
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gerundives for a total amount of so
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object+indirect object/object one.
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(VP (VB patch) ) ) ) (VP (VBZ is) (
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Types Adverb. Adject. Verbs Nouns T
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Eventually we may comment that ther
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DeepBank: A Dynamically Annotated T
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from another already existing one,
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to parse now does. The extra manual
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In the derivation show in Figure 1,
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4 Patching Coverage Gaps with An Ap
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will enable further improvements in
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ParDeepBank: Multiple Parallel Deep
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2 The ParDeepBank The PTB has emerg
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Page 149 and 150: 6 Conclusions In this paper we have
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quite high. This motivates us towar
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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