In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Q: To further improve the performance, we adopt system combination on the decoding lattice level. By combining systems, we can take advantage of the strength of each model that is optimized for different domains. The best result for vlsp2018 of 4.85% WER is obtained by the combination weights 0.6:0.4 where 0.6 is given to the general language model and 0.4 is given to the conversation one. On the vlsp2019 set, the ratio is change slightly by 0.7:0.3 to deliver the best result of 15.09%.
Question: What is the language model combination technique used in the paper?

A: system combination on the decoding lattice level combination weights
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Q: We train language models on two languages: One model is estimated on the German newscrawl distributed by WMT'18 comprising 260M sentences or 6B tokens. Another model is trained on the English newscrawl data comprising 193M sentences or 5B tokens. We learn a joint Byte-Pair-Encoding (BPE; Sennrich et al., 2016) vocabulary of 37K types on the German and English newscrawl and train the language models with this vocabulary. We consider two benchmarks: Most experiments are run on the WMT'18 English-German (en-de) news translation task and we validate our findings on the WMT'18 English-Turkish (en-tr) news task. For WMT'18 English-German, the training corpus consists of all available bitext excluding the ParaCrawl corpus and we remove sentences longer than 250 tokens as well as sentence-pairs with a source/target length ratio exceeding 1.5. This results in 5.18M sentence pairs. We tokenize all data with the Moses tokenizer BIBREF8 and apply the BPE vocabulary learned on the monolingual corpora.
Question: What dataset do they use?

A: German newscrawl distributed by WMT'18 English newscrawl data WMT'18 English-German (en-de) news translation task WMT'18 English-Turkish (en-tr) news task
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Q: For a given query $q = \langle s, r, ?
angle $ , we identify mentions in $S_q$ of the entities in $C_q \cup \lbrace s
brace $ and create one node per mention. This process is based on the following heuristic:

we consider mentions spans in $S_q$ exactly matching an element of $C_q \cup \lbrace s
brace $ . Admittedly, this is a rather simple strategy which may suffer from low recall.

we use predictions from a coreference resolution system to add mentions of elements in $C_q \cup \lbrace s
brace $ beyond exact matching (including both noun phrases and anaphoric pronouns). In particular, we use the end-to-end coreference resolution by BIBREF16 .

we discard mentions which are ambiguously resolved to multiple coreference chains; this may sacrifice recall, but avoids propagating ambiguity.
Question: How did they detect entity mentions?

A:
Exact matches to the entity string and predictions from a coreference resolution system
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